title

PyTorch Basics and Gradient Descent | Deep Learning with PyTorch: Zero to GANs | Part 1 of 6

description

“Deep Learning with PyTorch: Zero to GANs” is a beginner-friendly online course offering a practical and coding-focused introduction to deep learning using the PyTorch framework. Learn more and register for a certificate of accomplishment here: http://zerotogans.com/
Watch the entire series here: https://www.youtube.com/playlist?list=PLWKjhJtqVAbm5dir5TLEy2aZQMG7cHEZp
Code and Resources:
PyTorch basics: https://jovian.ai/aakashns/01-pytorch-basics
Linear regression: https://jovian.ai/aakashns/02-linear-regression
Machine learning: https://jovian.ai/aakashns/machine-learning-intro
Discussion forum: https://jovian.ai/forum/c/pytorch-zero-to-gans/lecture-1-pytorch-basics-linear-regression/63
Topics covered in this video:
Introduction to machine learning and Jupyter notebooks
PyTorch basics: tensors, gradients, and autograd
Linear regression & gradient descent from scratch
Using PyTorch modules: nn.Linear & nn.functional
This course is taught by Aakash N S, co-founder & CEO of Jovian - a data science platform and global community.
- YouTube: https://youtube.com/jovianml
- Twitter: https://twitter.com/jovianml
- LinkedIn: https://linkedin.com/company/jovianml
--
Learn to code for free and get a developer job: https://www.freecodecamp.org
Read hundreds of articles on programming: https://freecodecamp.org/news

detail

{'title': 'PyTorch Basics and Gradient Descent | Deep Learning with PyTorch: Zero to GANs | Part 1 of 6', 'heatmap': [{'end': 858.976, 'start': 786.029, 'weight': 0.858}, {'end': 2498.352, 'start': 2227.066, 'weight': 0.756}, {'end': 3081.742, 'start': 2948.296, 'weight': 0.718}, {'end': 3475.375, 'start': 3401.213, 'weight': 0.701}, {'end': 4724.931, 'start': 4129.645, 'weight': 0.828}, {'end': 4856.376, 'start': 4784.724, 'weight': 0.75}], 'summary': 'The 6-week deep learning with pytorch zero to gans course covers jupyter notebooks, pytorch tensors, derivatives, tensor operations, pytorch-numpy interoperability, linear regression fundamentals, msc loss function, model improvement, and pytorch for model training. it emphasizes ease of implementation, gpu support, and efficient built-in functions for model creation and training.', 'chapters': [{'end': 358.572, 'segs': [{'end': 38.232, 'src': 'embed', 'start': 10.356, 'weight': 0, 'content': [{'end': 13.38, 'text': 'Hello, and welcome to deep learning with PyTorch zero to GANs.', 'start': 10.356, 'duration': 3.024}, {'end': 18.686, 'text': 'This is a live online certification course offered in collaboration by FreeCodeCamp and Jovian.', 'start': 13.48, 'duration': 5.206}, {'end': 26.956, 'text': 'Over the next six weeks, you will learn deep learning using the PyTorch framework, and I will be your instructor, Akash.', 'start': 20.108, 'duration': 6.848}, {'end': 30.349, 'text': 'So, by the end of this course,', 'start': 28.988, 'duration': 1.361}, {'end': 38.232, 'text': 'you will be able to train a model which goes from producing random noise to fairly good images of handwritten digits and anime faces.', 'start': 30.349, 'duration': 7.883}], 'summary': 'Learn pytorch deep learning in 6 weeks to create images of digits and anime faces.', 'duration': 27.876, 'max_score': 10.356, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM10356.jpg'}, {'end': 84.729, 'src': 'embed', 'start': 51.839, 'weight': 1, 'content': [{'end': 54.58, 'text': 'And we will learn all about them starting from the very basics.', 'start': 51.839, 'duration': 2.741}, {'end': 56.649, 'text': 'Not only that,', 'start': 55.529, 'duration': 1.12}, {'end': 63.951, 'text': 'you will be able to build a real world project using deep learning and earn a verified certificate of accomplishment from Jovian and free code camp.', 'start': 56.649, 'duration': 7.302}, {'end': 71.034, 'text': "I'm really excited to kick off this course.", 'start': 69.313, 'duration': 1.721}, {'end': 72.434, 'text': "So let's get started.", 'start': 71.554, 'duration': 0.88}, {'end': 79.004, 'text': 'The first thing you need to do is go to zero to gans.com, which will bring you to this course page.', 'start': 73.8, 'duration': 5.204}, {'end': 84.729, 'text': 'Now on the course page, you can click the enroll button to enroll for the course and share button to invite your friends.', 'start': 79.425, 'duration': 5.304}], 'summary': 'Learn deep learning, build project, earn certificate from jovian & free code camp', 'duration': 32.89, 'max_score': 51.839, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM51839.jpg'}, {'end': 162.652, 'src': 'embed', 'start': 139.092, 'weight': 2, 'content': [{'end': 146.415, 'text': 'Once again, you just need to know about vectors, matrices, derivatives, and probabilities, and you can follow these links to learn about these topics.', 'start': 139.092, 'duration': 7.323}, {'end': 151.58, 'text': 'So in a couple of hours, you should be able to cover all of the prerequisites that you need for this course.', 'start': 147.455, 'duration': 4.125}, {'end': 157.887, 'text': 'And any additional mathematical or theoretical concepts that we need, we will cover as we go along.', 'start': 152.681, 'duration': 5.206}, {'end': 162.652, 'text': 'There is no prior knowledge of data science or deep learning required for taking this course.', 'start': 158.407, 'duration': 4.245}], 'summary': 'Prerequisites for the course are vectors, matrices, derivatives, and probabilities, which can be covered in a couple of hours. no prior knowledge of data science or deep learning is required.', 'duration': 23.56, 'max_score': 139.092, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM139092.jpg'}, {'end': 208.666, 'src': 'embed', 'start': 179.371, 'weight': 3, 'content': [{'end': 184.452, 'text': 'If you want to run the code, the easiest way to do that is using free online resources,', 'start': 179.371, 'duration': 5.081}, {'end': 188.073, 'text': 'but you can also run it on your computer locally and you will find some instructions here.', 'start': 184.452, 'duration': 3.621}, {'end': 190.614, 'text': 'So what you need to do is scroll up.', 'start': 188.633, 'duration': 1.981}, {'end': 194.176, 'text': 'Find the run button here and click run on Colab.', 'start': 191.494, 'duration': 2.682}, {'end': 202.362, 'text': 'So this will give you an option to authorize your Google drive access and run this notebook on Colab.', 'start': 195.517, 'duration': 6.845}, {'end': 208.666, 'text': 'So just click on authorize and you will be asked to select a Google account here,', 'start': 202.802, 'duration': 5.864}], 'summary': 'Easily run the code using free online resources or your local computer with provided instructions.', 'duration': 29.295, 'max_score': 179.371, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM179371.jpg'}, {'end': 307.43, 'src': 'embed', 'start': 277.552, 'weight': 4, 'content': [{'end': 281.675, 'text': 'So there is a, this is a cell within a Jupiter notebook where there is some code.', 'start': 277.552, 'duration': 4.123}, {'end': 287.56, 'text': 'If you click the run button here or press shift plus enter, this will run the code for you.', 'start': 282.235, 'duration': 5.325}, {'end': 290.722, 'text': 'So make sure you run the first cell.', 'start': 289.101, 'duration': 1.621}, {'end': 293.705, 'text': 'Otherwise your notebook may not function properly.', 'start': 291.203, 'duration': 2.502}, {'end': 299.53, 'text': 'And the first time you run it, it may take a minute or two just to initialize.', 'start': 295.706, 'duration': 3.824}, {'end': 301.051, 'text': "So let's give it that time.", 'start': 299.99, 'duration': 1.061}, {'end': 307.43, 'text': 'All right.', 'start': 307.13, 'duration': 0.3}], 'summary': "A cell within a jupiter notebook runs code when 'shift plus enter' is pressed, taking a minute or two to initialize.", 'duration': 29.878, 'max_score': 277.552, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM277552.jpg'}, {'end': 357.131, 'src': 'embed', 'start': 330.147, 'weight': 5, 'content': [{'end': 333.531, 'text': 'And in this tutorial, we will cover the, the following topics.', 'start': 330.147, 'duration': 3.384}, {'end': 335.514, 'text': 'We will learn about PyTorch tensors.', 'start': 333.732, 'duration': 1.782}, {'end': 338.057, 'text': 'We will learn about tensors operations and gradients.', 'start': 335.634, 'duration': 2.423}, {'end': 344.706, 'text': 'We will learn about the interoperability between PyTorch and NumPy, and we will learn how to use the PyTorch documentation website.', 'start': 338.458, 'duration': 6.248}, {'end': 348.83, 'text': "And we've already seen how to run the code so we can skip ahead.", 'start': 346.229, 'duration': 2.601}, {'end': 353.23, 'text': 'There is, there are some instructions here to install the required libraries.', 'start': 349.85, 'duration': 3.38}, {'end': 357.131, 'text': 'Now, if you are running on Google Colab, you do not need to install anything.', 'start': 353.651, 'duration': 3.48}], 'summary': 'Tutorial covers pytorch tensors, operations, numpy interoperability and documentation usage.', 'duration': 26.984, 'max_score': 330.147, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM330147.jpg'}], 'start': 10.356, 'title': 'Deep learning and jupyter notebooks', 'summary': 'Covers a six-week deep learning with pytorch zero to gans course, enabling model training for handwritten digits and anime faces, and jupyter notebooks on the jovian platform, providing instructions on running it online or locally and covering basic features and topics.', 'chapters': [{'end': 157.887, 'start': 10.356, 'title': 'Deep learning with pytorch', 'summary': 'Introduces the deep learning with pytorch zero to gans course, covering a six-week program to learn deep learning using pytorch, with the ability to train a model to produce images of handwritten digits and anime faces, and the opportunity to build a real-world project and earn a verified certificate of accomplishment.', 'duration': 147.531, 'highlights': ['The course is a six-week program to learn deep learning using PyTorch, with the ability to train a model to produce images of handwritten digits and anime faces. The course spans over six weeks and enables the trainees to produce images of handwritten digits and anime faces using the trained model.', 'Opportunity to build a real-world project and earn a verified certificate of accomplishment from Jovian and free code camp is provided. Participants have the chance to develop a real-world project and obtain a verified certificate of accomplishment from Jovian and free code camp.', 'The prerequisites include a little bit of programming with Python and high school mathematics knowledge, covering topics like vectors, matrices, derivatives, and probabilities. The prerequisites involve a basic understanding of Python programming and high school mathematics, including vectors, matrices, derivatives, and probabilities.']}, {'end': 358.572, 'start': 158.407, 'title': 'Running jupyter notebooks on jovian platform', 'summary': 'Explains how to run jupyter notebooks on the jovian platform, including using free online resources and running it on a local computer, with instructions to authorize access and how to clear outputs, while covering the basics of jupyter notebooks and the topics that will be covered in the tutorial.', 'duration': 200.165, 'highlights': ['Explanation on how to run Jupyter notebooks on the Jovian platform Includes using free online resources and running it on a local computer, with instructions to authorize access and how to clear outputs', 'Basics of Jupyter notebooks Describes the structure of a Jupyter notebook, how to run code cells, create new cells, and execute the first cell', 'Topics covered in the PyTorch basics tutorial Covers PyTorch tensors, tensor operations and gradients, interoperability with NumPy, and using the PyTorch documentation website']}], 'duration': 348.216, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM10356.jpg', 'highlights': ['The course is a six-week program to learn deep learning using PyTorch, with the ability to train a model to produce images of handwritten digits and anime faces.', 'Opportunity to build a real-world project and earn a verified certificate of accomplishment from Jovian and free code camp is provided.', 'The prerequisites include a little bit of programming with Python and high school mathematics knowledge, covering topics like vectors, matrices, derivatives, and probabilities.', 'Explanation on how to run Jupyter notebooks on the Jovian platform Includes using free online resources and running it on a local computer, with instructions to authorize access and how to clear outputs', 'Basics of Jupyter notebooks Describes the structure of a Jupyter notebook, how to run code cells, create new cells, and execute the first cell', 'Topics covered in the PyTorch basics tutorial Covers PyTorch tensors, tensor operations and gradients, interoperability with NumPy, and using the PyTorch documentation website']}, {'end': 1033.637, 'segs': [{'end': 402.963, 'src': 'embed', 'start': 376.585, 'weight': 0, 'content': [{'end': 381.068, 'text': "So let's import PyTorch and the way you import PyTorch is by writing import torch.", 'start': 376.585, 'duration': 4.483}, {'end': 386.071, 'text': 'So import torch imports the torch module, which contains all the functionality of PyTorch.', 'start': 381.288, 'duration': 4.783}, {'end': 388.052, 'text': 'So now we have access to the torch module.', 'start': 386.211, 'duration': 1.841}, {'end': 393.115, 'text': 'And at its core, PyTorch is a library for processing tensors.', 'start': 389.513, 'duration': 3.602}, {'end': 398.258, 'text': 'A tensor is a number, a vector, a matrix, or any n dimensional array.', 'start': 393.535, 'duration': 4.723}, {'end': 401.701, 'text': "So let's create a tensor with a single number.", 'start': 399.639, 'duration': 2.062}, {'end': 402.963, 'text': 'This is how you do it.', 'start': 402.162, 'duration': 0.801}], 'summary': 'Pytorch is a library for processing tensors, which are n-dimensional arrays.', 'duration': 26.378, 'max_score': 376.585, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM376585.jpg'}, {'end': 541.912, 'src': 'embed', 'start': 521.019, 'weight': 1, 'content': [{'end': 530.925, 'text': 'Now, the reason we use floating point numbers and floating point tensors for deep learning is because a lot of the operations that we will be performing will not yield integer results.', 'start': 521.019, 'duration': 9.906}, {'end': 538.69, 'text': 'For example, we will be doing matrix multiplications and divisions and inversions and things like that and gradients and so on.', 'start': 531.986, 'duration': 6.704}, {'end': 541.912, 'text': 'And all of these will not produce integer results.', 'start': 539.07, 'duration': 2.842}], 'summary': 'Floating point numbers are used for deep learning operations due to non-integer results from matrix operations and gradients.', 'duration': 20.893, 'max_score': 521.019, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM521019.jpg'}, {'end': 632.173, 'src': 'embed', 'start': 608.404, 'weight': 2, 'content': [{'end': 615.932, 'text': "That's a three-dimensional tensor and tensors can have any number of dimensions and different lengths along each dimensions.", 'start': 608.404, 'duration': 7.528}, {'end': 621.157, 'text': 'So we can expect the length along each dimension of a tensor using the dot shape property.', 'start': 616.572, 'duration': 4.585}, {'end': 625.508, 'text': "Let's see the tensors we've created so far and their shapes.", 'start': 623.106, 'duration': 2.402}, {'end': 628.73, 'text': 'So the T1 tensor was simply a number four.', 'start': 625.988, 'duration': 2.742}, {'end': 630.592, 'text': 'So it did not really have any shape.', 'start': 629.131, 'duration': 1.461}, {'end': 632.173, 'text': 'In fact, it has zero dimensions.', 'start': 630.752, 'duration': 1.421}], 'summary': 'Tensors can have any number of dimensions and lengths, t1 tensor has zero dimensions.', 'duration': 23.769, 'max_score': 608.404, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM608404.jpg'}, {'end': 858.976, 'src': 'heatmap', 'start': 764.983, 'weight': 5, 'content': [{'end': 769.484, 'text': 'The other difference being that all the elements within a tensor should have the same data type.', 'start': 764.983, 'duration': 4.501}, {'end': 774.686, 'text': 'If they do not have the same data type, they will be given the same data type while creating the tensor.', 'start': 770.164, 'duration': 4.522}, {'end': 777.886, 'text': "Okay So that's about tensors.", 'start': 776.046, 'duration': 1.84}, {'end': 785.448, 'text': 'Now we can combine tensors using the usual arithmetic operations that we use for numbers.', 'start': 780.067, 'duration': 5.381}, {'end': 787.209, 'text': "So let's look at an example.", 'start': 786.029, 'duration': 1.18}, {'end': 790.907, 'text': 'Here we have X, W and B.', 'start': 788.564, 'duration': 2.343}, {'end': 792.128, 'text': 'We are creating three tensors.', 'start': 790.907, 'duration': 1.221}, {'end': 795.051, 'text': 'They have the values three, four and five respectively.', 'start': 792.528, 'duration': 2.523}, {'end': 801.959, 'text': "Now we've added this special argument here called requires grad equals true.", 'start': 795.852, 'duration': 6.107}, {'end': 806.263, 'text': "What's that going to do? Well, we'll see in just a moment.", 'start': 803.28, 'duration': 2.983}, {'end': 807.465, 'text': "So let's run this right now.", 'start': 806.303, 'duration': 1.162}, {'end': 813.934, 'text': 'So now we have X, W and B with the values three, four, and five, as we expected.', 'start': 810.273, 'duration': 3.661}, {'end': 822.475, 'text': 'Now, if we want to combine these tensors to create a new tensor, why all we need to do is use the basic arithmetic operations that we already know.', 'start': 814.774, 'duration': 7.701}, {'end': 826.276, 'text': 'So just W multiplied by X.', 'start': 823.095, 'duration': 3.181}, {'end': 837.378, 'text': 'So the star indicates multiplication plus B, and you might expect this will give you three times four, 12 plus five 17.', 'start': 826.276, 'duration': 11.102}, {'end': 838.318, 'text': 'And it gives us 17 as we expect.', 'start': 837.378, 'duration': 0.94}, {'end': 846.913, 'text': 'Now, what makes PyTorch unique is that we can automatically compute the derivative of Y.', 'start': 841.104, 'duration': 5.809}, {'end': 854.233, 'text': 'Now, if you look at why, why is a function of w X and B, right? Why is w X plus B.', 'start': 847.748, 'duration': 6.485}, {'end': 858.976, 'text': "So you can take the derivative of Y with respect to w and let's do that mentally.", 'start': 854.233, 'duration': 4.743}], 'summary': 'Tensors in pytorch can be combined using arithmetic operations, and pytorch can automatically compute the derivative of the result.', 'duration': 25.924, 'max_score': 764.983, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM764983.jpg'}, {'end': 865.802, 'src': 'embed', 'start': 841.104, 'weight': 3, 'content': [{'end': 846.913, 'text': 'Now, what makes PyTorch unique is that we can automatically compute the derivative of Y.', 'start': 841.104, 'duration': 5.809}, {'end': 854.233, 'text': 'Now, if you look at why, why is a function of w X and B, right? Why is w X plus B.', 'start': 847.748, 'duration': 6.485}, {'end': 858.976, 'text': "So you can take the derivative of Y with respect to w and let's do that mentally.", 'start': 854.233, 'duration': 4.743}, {'end': 865.802, 'text': 'So w X plus B, the derivative of that would be the derivative of w X plus the derivative of B.', 'start': 859.357, 'duration': 6.445}], 'summary': 'Pytorch can automatically compute derivatives, useful for gradient descent.', 'duration': 24.698, 'max_score': 841.104, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM841104.jpg'}, {'end': 942.452, 'src': 'embed', 'start': 912.589, 'weight': 4, 'content': [{'end': 918.494, 'text': 'Because the technique that we use to train the machine learning models, the deep learning models,', 'start': 912.589, 'duration': 5.905}, {'end': 925.861, 'text': 'the technique that we use to train the model to produce those images that we looked at at the beginning, that requires computation of derivatives.', 'start': 918.494, 'duration': 7.367}, {'end': 927.922, 'text': 'It involves derivatives in some way.', 'start': 926.141, 'duration': 1.781}, {'end': 931.125, 'text': "And we'll see how today by the end of this lecture.", 'start': 928.843, 'duration': 2.282}, {'end': 933.246, 'text': "So that's why derivatives are important.", 'start': 931.785, 'duration': 1.461}, {'end': 942.452, 'text': 'And what PyTorch provides is if you want the derivative of Y with respect to W, all you need to do is you need to call Y dot backward.', 'start': 934.026, 'duration': 8.426}], 'summary': 'Training deep learning models requires computation of derivatives, which pytorch simplifies by providing a function for backward differentiation.', 'duration': 29.863, 'max_score': 912.589, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM912589.jpg'}], 'start': 358.572, 'title': 'Pytorch tensors and derivatives', 'summary': 'Introduces pytorch tensors and their properties, demonstrates creation and conversion of numbers into floating point tensors, covers tensor shapes and limitations, and explains operations on tensors with a focus on automatic computation of derivatives in pytorch, emphasizing their importance in training machine learning models.', 'chapters': [{'end': 541.912, 'start': 358.572, 'title': 'Introduction to pytorch tensors', 'summary': 'Introduces pytorch tensors, demonstrating their creation and properties, highlighting the conversion of numbers into floating point tensors and the importance of using floating point tensors for deep learning operations.', 'duration': 183.34, 'highlights': ['PyTorch is a library for processing tensors, which can be numbers, vectors, matrices, or n-dimensional arrays. PyTorch is a library for processing tensors, covering numbers, vectors, matrices, or n-dimensional arrays.', 'Demonstrated the creation of tensors with single numbers, conversion of numbers into floating point tensors, and the property that all elements of a tensor have the same type. Demonstrated the creation of tensors with single numbers, conversion into floating point tensors, and the property of uniform element type.', 'Importance of using floating point tensors for deep learning operations due to the nature of operations like matrix multiplications, divisions, inversions, and gradients. Emphasized the importance of using floating point tensors for deep learning operations involving matrix multiplications, divisions, inversions, and gradients.']}, {'end': 763.727, 'start': 542.472, 'title': 'Introduction to tensors', 'summary': 'Introduces the concept of tensors in python, covering the creation of tensors, their shapes, and limitations, with an example of a three-dimensional tensor having a shape of 2, 2, and 3.', 'duration': 221.255, 'highlights': ['Tensors can have any number of dimensions and different lengths along each dimension. Tensors can have any number of dimensions and different lengths along each dimension, demonstrated through the example of a three-dimensional tensor with a shape of 2, 2, and 3.', 'Explanation of creating a three-dimensional tensor using matrices and representing it as a cuboid structure. The process of creating a three-dimensional tensor using matrices and representing it as a cuboid structure is explained, providing a visual understanding of the concept.', 'Demonstration of creating a three-dimensional tensor with a shape of 2, 2, and 3. An example of creating a three-dimensional tensor with a shape of 2, 2, and 3 is demonstrated, emphasizing the demonstration of tensor creation with specified dimensions.']}, {'end': 1033.637, 'start': 764.983, 'title': 'Pytorch tensors and derivatives', 'summary': 'Explains the concept of tensors, operations on tensors, and automatic computation of derivatives in pytorch, with a focus on the importance of derivatives in training machine learning models.', 'duration': 268.654, 'highlights': ['PyTorch allows automatic computation of derivatives for tensors, which is crucial in training machine learning models. PyTorch provides the capability to automatically compute derivatives of a function with respect to its inputs, which is essential in the training of machine learning models.', 'The importance of derivatives in training machine learning models is emphasized, as it involves derivatives in some way and derivatives of future outputs with respect to specific inputs can be optimized using the requires grad property. Derivatives play a crucial role in training machine learning models, and the use of the requires grad property allows for optimization by specifying the interest in derivatives of future outputs with respect to specific inputs.', 'Basic arithmetic operations can be used to combine tensors, and PyTorch enables the computation of derivatives of the combined tensors with respect to their individual components. PyTorch supports basic arithmetic operations for combining tensors, and it facilitates the computation of derivatives of the combined tensors with respect to their individual components.']}], 'duration': 675.065, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM358572.jpg', 'highlights': ['PyTorch is a library for processing tensors, covering numbers, vectors, matrices, or n-dimensional arrays.', 'Importance of using floating point tensors for deep learning operations involving matrix multiplications, divisions, inversions, and gradients.', 'Demonstration of creating a three-dimensional tensor with a shape of 2, 2, and 3, emphasizing the demonstration of tensor creation with specified dimensions.', 'PyTorch provides the capability to automatically compute derivatives of a function with respect to its inputs, which is essential in the training of machine learning models.', 'Derivatives play a crucial role in training machine learning models, and the use of the requires grad property allows for optimization by specifying the interest in derivatives of future outputs with respect to specific inputs.', 'PyTorch supports basic arithmetic operations for combining tensors, and it facilitates the computation of derivatives of the combined tensors with respect to their individual components.']}, {'end': 1744.322, 'segs': [{'end': 1098.848, 'src': 'embed', 'start': 1051.608, 'weight': 0, 'content': [{'end': 1058.372, 'text': 'And the term gradient is primarily used while dealing with vectors and matrices and their derivatives and partial derivatives and so on.', 'start': 1051.608, 'duration': 6.764}, {'end': 1067.019, 'text': 'So that was basic tensor operations and tensor arithmetic.', 'start': 1063.038, 'duration': 3.981}, {'end': 1073.861, 'text': 'Apart from arithmetic operations, the torch module also contains many functions for creating and manipulating tensors.', 'start': 1068.08, 'duration': 5.781}, {'end': 1075.442, 'text': "So let's look at some examples here.", 'start': 1074.001, 'duration': 1.441}, {'end': 1078.343, 'text': "Let's look at this example of a function called full.", 'start': 1076.162, 'duration': 2.181}, {'end': 1088.626, 'text': 'So you say torch dot full and you give it a shape and then you give it a value and then it creates a tensor with that value with the given shape.', 'start': 1079.563, 'duration': 9.063}, {'end': 1091.327, 'text': 'So that value is repeated everywhere.', 'start': 1089.846, 'duration': 1.481}, {'end': 1095.765, 'text': 'Similarly, we have this another tensor called cat.', 'start': 1093.243, 'duration': 2.522}, {'end': 1098.848, 'text': 'So what this is going to do is this is going to join the tensors.', 'start': 1096.165, 'duration': 2.683}], 'summary': 'Tensor operations, arithmetic, and functions in torch module.', 'duration': 47.24, 'max_score': 1051.608, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1051608.jpg'}, {'end': 1278.995, 'src': 'embed', 'start': 1251.317, 'weight': 6, 'content': [{'end': 1256.942, 'text': 'It has a library called matplotlib for plotting and visualization and open CV for image and video processing.', 'start': 1251.317, 'duration': 5.625}, {'end': 1261.962, 'text': 'Now, this is a huge topic in itself, data science with Python.', 'start': 1258.339, 'duration': 3.623}, {'end': 1269.007, 'text': 'And if you are interested in learning more about NumPy and other data science libraries in Python, then you can check out this tutorial series.', 'start': 1262.542, 'duration': 6.465}, {'end': 1278.995, 'text': 'We also have a full video course that you can take over six weeks, and you can take that course side by side by going to zerotopandas.com, that is Z,', 'start': 1269.408, 'duration': 9.587}], 'summary': 'Python offers libraries like matplotlib and opencv for data visualization and image processing. explore data science with python through tutorials and a six-week video course at zerotopandas.com.', 'duration': 27.678, 'max_score': 1251.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1251317.jpg'}, {'end': 1444.544, 'src': 'embed', 'start': 1402.799, 'weight': 1, 'content': [{'end': 1409.101, 'text': 'because most of the datasets that you work with will likely be read and processed using NumPy arrays,', 'start': 1402.799, 'duration': 6.302}, {'end': 1412.423, 'text': 'and any data analysis course or tutorial you take will be using NumPy.', 'start': 1409.101, 'duration': 3.322}, {'end': 1417.064, 'text': 'So you might wonder at this point why we need a library like PyTorch at all,', 'start': 1412.963, 'duration': 4.101}, {'end': 1421.866, 'text': 'since NumPy provides all the data structures and utilities for working with multidimensional data right?', 'start': 1417.064, 'duration': 4.802}, {'end': 1423.607, 'text': 'So there are two main reasons here.', 'start': 1422.166, 'duration': 1.441}, {'end': 1430.332, 'text': 'One is Autograd, which is the ability of PyTorch to automatically compute gradients for tensor operations.', 'start': 1424.247, 'duration': 6.085}, {'end': 1432.054, 'text': 'This is essential for deep learning.', 'start': 1430.512, 'duration': 1.542}, {'end': 1434.175, 'text': 'And the second is GPU support.', 'start': 1432.634, 'duration': 1.541}, {'end': 1439.12, 'text': 'So, when we are working with massive data sets and large models, which is GBs and GBs of data,', 'start': 1434.236, 'duration': 4.884}, {'end': 1444.544, 'text': 'PyTorch tensor operations can be performed very efficiently using graphics processing units or GPUs.', 'start': 1439.12, 'duration': 5.424}], 'summary': 'Pytorch offers autograd for gradient computation and gpu support for efficient tensor operations on large datasets and models.', 'duration': 41.745, 'max_score': 1402.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1402799.jpg'}, {'end': 1505.532, 'src': 'embed', 'start': 1471.158, 'weight': 3, 'content': [{'end': 1473.999, 'text': "So what you're looking at right now, Google collab.", 'start': 1471.158, 'duration': 2.841}, {'end': 1475.84, 'text': 'this is going to shut down after some time.', 'start': 1473.999, 'duration': 1.841}, {'end': 1479.521, 'text': 'this is running on the cloud and this is stored privately in your Google account.', 'start': 1475.84, 'duration': 3.681}, {'end': 1483.923, 'text': 'Now what you can do is you can take this notebook and put it onto your Jovian account,', 'start': 1479.881, 'duration': 4.042}, {'end': 1489.205, 'text': 'the same account that you used that you created while enrolling for the course or while running this notebook.', 'start': 1483.923, 'duration': 5.282}, {'end': 1495.668, 'text': 'So all you need to do to put this notebook onto your Jovian account, is run pip install Jovian.', 'start': 1489.685, 'duration': 5.983}, {'end': 1505.532, 'text': 'So this is going to install the Jovian Python library, import the Jovian library, and then say Jovian dot commit and give it a project name.', 'start': 1496.208, 'duration': 9.324}], 'summary': 'Google collab runs on cloud, store notebook privately. install jovian, commit to project.', 'duration': 34.374, 'max_score': 1471.158, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1471158.jpg'}, {'end': 1614.879, 'src': 'embed', 'start': 1584.997, 'weight': 4, 'content': [{'end': 1586.779, 'text': "So just summarize what we've learned here.", 'start': 1584.997, 'duration': 1.782}, {'end': 1594.647, 'text': 'This tutorial covers introduction to pytorch tensors, tensor operations and gradients and interoperability between pytorch and numpy.', 'start': 1587.039, 'duration': 7.608}, {'end': 1602.536, 'text': 'Now this is all fairly simple stuff, but we are learning this because this is giving us the foundation to pick up and learn the next topic,', 'start': 1595.048, 'duration': 7.488}, {'end': 1604.718, 'text': 'which is gradient descent and linear regression.', 'start': 1602.536, 'duration': 2.182}, {'end': 1606.14, 'text': 'So let me open that up.', 'start': 1605.239, 'duration': 0.901}, {'end': 1614.879, 'text': 'Now, one thing you can do here is there are a bunch of questions, almost 30, 32 questions here at the end of this notebook.', 'start': 1607.816, 'duration': 7.063}], 'summary': 'Introduction to pytorch tensors, operations, and gradients, with 30-32 questions for practice.', 'duration': 29.882, 'max_score': 1584.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1584997.jpg'}], 'start': 1033.818, 'title': 'Tensor operations and pytorch-numpy interoperability', 'summary': 'Covers basic tensor operations, tensor derivatives, and introduces tensor functions like full, cat, and sign. it also explores the interoperability between pytorch and numpy, emphasizing the advantages for deep learning, autograd, gpu support, and collaboration on jovian.', 'chapters': [{'end': 1291.349, 'start': 1033.818, 'title': 'Tensor operations and torch module', 'summary': 'Covers basic tensor operations and arithmetic, including tensor derivatives and partial derivatives, and introduces tensor functions like full, cat, and sign, with a mention of the availability of close to a thousand tensor operations in the torch module.', 'duration': 257.531, 'highlights': ['The torch module contains close to a thousand tensor operations, including functions for creating and manipulating tensors. The torch module offers a wide range of tensor operations, including functions for creating and manipulating tensors, with close to a thousand operations available.', 'Introduction to tensor functions like full, cat, and sign, for creating and manipulating tensors. The chapter introduces tensor functions such as full, cat, and sign, which allow for the creation and manipulation of tensors.', 'Explanation of the torch dot cat function for concatenating or joining two tensors with compatible shapes into a single tensor. The torch dot cat function is explained, demonstrating its ability to join two tensors with compatible shapes into a single tensor.', 'Mention of the availability of a vast ecosystem of supporting libraries for NumPy, including pandas for file IO and data analysis, matplotlib for plotting and visualization, and open CV for image and video processing. The transcript mentions the vast ecosystem of supporting libraries for NumPy, such as pandas for file IO and data analysis, matplotlib for plotting and visualization, and open CV for image and video processing.']}, {'end': 1744.322, 'start': 1291.349, 'title': 'Pytorch-numpy interoperability', 'summary': 'Covers the interoperability between pytorch and numpy, demonstrating the creation of numpy arrays and their conversion to pytorch tensors, highlighting the importance of this interoperability for deep learning, emphasizing the advantages of pytorch in terms of autograd and gpu support, and concluding with a guide on how to save the notebook on jovian for further collaboration and sharing.', 'duration': 452.973, 'highlights': ['The interoperability between PyTorch and NumPy is essential, as most datasets are likely processed using NumPy arrays, and any data analysis course or tutorial will be using NumPy. The interoperability between PyTorch and NumPy is essential, as most datasets are likely processed using NumPy arrays, and any data analysis course or tutorial will be using NumPy.', 'PyTorch provides significant benefits in terms of Autograd, enabling automatic computation of gradients for tensor operations, essential for deep learning, and GPU support, allowing efficient processing of massive datasets and large models using GPUs, significantly reducing computation time. PyTorch provides significant benefits in terms of Autograd, enabling automatic computation of gradients for tensor operations, essential for deep learning, and GPU support, allowing efficient processing of massive datasets and large models using GPUs, significantly reducing computation time.', 'The tutorial emphasizes the importance of the foundation laid by understanding PyTorch tensors and operations, highlighting their role in learning subsequent topics such as gradient descent and linear regression. The tutorial emphasizes the importance of the foundation laid by understanding PyTorch tensors and operations, highlighting their role in learning subsequent topics such as gradient descent and linear regression.', 'A guide on saving the notebook on Jovian is provided, enabling further collaboration and sharing while ensuring version control of the notebook. A guide on saving the notebook on Jovian is provided, enabling further collaboration and sharing while ensuring version control of the notebook.']}], 'duration': 710.504, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1033818.jpg', 'highlights': ['The torch module offers a wide range of tensor operations, including functions for creating and manipulating tensors, with close to a thousand operations available.', 'PyTorch provides significant benefits in terms of Autograd, enabling automatic computation of gradients for tensor operations, essential for deep learning, and GPU support, allowing efficient processing of massive datasets and large models using GPUs, significantly reducing computation time.', 'The interoperability between PyTorch and NumPy is essential, as most datasets are likely processed using NumPy arrays, and any data analysis course or tutorial will be using NumPy.', 'A guide on saving the notebook on Jovian is provided, enabling further collaboration and sharing while ensuring version control of the notebook.', 'The tutorial emphasizes the importance of the foundation laid by understanding PyTorch tensors and operations, highlighting their role in learning subsequent topics such as gradient descent and linear regression.', 'The torch dot cat function is explained, demonstrating its ability to join two tensors with compatible shapes into a single tensor.', 'The transcript mentions the vast ecosystem of supporting libraries for NumPy, such as pandas for file IO and data analysis, matplotlib for plotting and visualization, and open CV for image and video processing.', 'Introduction to tensor functions like full, cat, and sign, for creating and manipulating tensors. The chapter introduces tensor functions such as full, cat, and sign, which allow for the creation and manipulation of tensors.']}, {'end': 2918.683, 'segs': [{'end': 1865.21, 'src': 'embed', 'start': 1836.705, 'weight': 0, 'content': [{'end': 1840.847, 'text': 'If you take any machine learning course, they will almost always begin with linear regression.', 'start': 1836.705, 'duration': 4.142}, {'end': 1843.028, 'text': 'And so is the case for deep learning.', 'start': 1841.548, 'duration': 1.48}, {'end': 1847.211, 'text': "In fact, linear regression is very closely related to what you're doing deep learning.", 'start': 1843.129, 'duration': 4.082}, {'end': 1853.415, 'text': 'So make sure to understand it properly, ask questions, rewatch the video, run the notebook,', 'start': 1847.271, 'duration': 6.144}, {'end': 1858.938, 'text': 'but make sure that you understand linear regression and gradient descent well, and that will set you up really well for the rest of the course.', 'start': 1853.415, 'duration': 5.523}, {'end': 1865.21, 'text': "Now, what we'll do is we will take an example problem and work through it to understand what linear regression is.", 'start': 1860.387, 'duration': 4.823}], 'summary': 'Linear regression is crucial for machine learning and deep learning, ensuring a strong foundation for the rest of the course.', 'duration': 28.505, 'max_score': 1836.705, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1836705.jpg'}, {'end': 1901.44, 'src': 'embed', 'start': 1874.035, 'weight': 1, 'content': [{'end': 1879.198, 'text': 'this model will predict these crop yields by looking at the average temperature, rainfall and humidity.', 'start': 1874.035, 'duration': 5.163}, {'end': 1882.899, 'text': 'in a region, and these are called the input variables.', 'start': 1880.177, 'duration': 2.722}, {'end': 1884.981, 'text': 'So here are some training data.', 'start': 1883.62, 'duration': 1.361}, {'end': 1887.383, 'text': "Let's suppose we've gone to five regions.", 'start': 1885.441, 'duration': 1.942}, {'end': 1895.57, 'text': "We've done surveys over the past few years and we've come up with this information that in the canto region the temperature was 73 degrees Fahrenheit.", 'start': 1887.423, 'duration': 8.147}, {'end': 1901.44, 'text': 'The average temperature, the rainfall was 67 and the humidity was 43%.', 'start': 1895.61, 'duration': 5.83}], 'summary': 'Model predicts crop yields based on temperature, rainfall, and humidity in different regions.', 'duration': 27.405, 'max_score': 1874.035, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1874035.jpg'}, {'end': 2014.099, 'src': 'embed', 'start': 1979.346, 'weight': 7, 'content': [{'end': 1988.991, 'text': 'Okay So the yield of apples is a V is a linear combination of the temperature, rainfall, and humidity using some weights and a bias.', 'start': 1979.346, 'duration': 9.645}, {'end': 1995.815, 'text': 'Similarly, the yield of oranges is another linear combination of temperature, rainfall, and humidity, but this time.', 'start': 1989.472, 'duration': 6.343}, {'end': 2014.099, 'text': 'But this time we are using different weights and visually what this means is if you plotted the temperature and rainfall on two axes and then the apples on the third axis and we are not looking at humidity here because it is not possible to plot in four dimensions.', 'start': 1998.189, 'duration': 15.91}], 'summary': "Apple yield depends on temperature, rainfall, humidity. oranges have different weights. can't plot humidity in 4d.", 'duration': 34.753, 'max_score': 1979.346, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1979346.jpg'}, {'end': 2167.505, 'src': 'embed', 'start': 2120.351, 'weight': 2, 'content': [{'end': 2123.293, 'text': 'Prediction of crop yields using weather, especially.', 'start': 2120.351, 'duration': 2.942}, {'end': 2129.656, 'text': "So that's what the learning process involves figuring out a good set of weights.", 'start': 2125.393, 'duration': 4.263}, {'end': 2133.359, 'text': 'And the way we will do this is we will train our model.', 'start': 2129.816, 'duration': 3.543}, {'end': 2137.102, 'text': "We'll train our model by adjusting the weights slightly many times.", 'start': 2133.859, 'duration': 3.243}, {'end': 2138.523, 'text': 'So we start out with random weights.', 'start': 2137.122, 'duration': 1.401}, {'end': 2139.964, 'text': 'Our model will do very badly.', 'start': 2138.703, 'duration': 1.261}, {'end': 2146.949, 'text': 'It will make bad predictions, but we will improve the weight slowly, using an optimization technique called gradient descent,', 'start': 2140.044, 'duration': 6.905}, {'end': 2150.711, 'text': 'which is at the heart of not just linear regression but all of deep learning.', 'start': 2146.949, 'duration': 3.762}, {'end': 2154.194, 'text': "So let's begin by importing NumPy and PyTorch.", 'start': 2151.872, 'duration': 2.322}, {'end': 2167.505, 'text': 'So now here we have the training data, the table that you saw earlier that can be represented using two matrices, inputs and targets.', 'start': 2160.802, 'duration': 6.703}], 'summary': 'Predict crop yields using weather, train model with gradient descent and data matrices.', 'duration': 47.154, 'max_score': 2120.351, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2120351.jpg'}, {'end': 2216.46, 'src': 'embed', 'start': 2187.894, 'weight': 4, 'content': [{'end': 2191.037, 'text': 'And we learn about those things over the next few lessons and assignments.', 'start': 2187.894, 'duration': 3.143}, {'end': 2197.341, 'text': "But for now we've assumed that somehow we have put together a numpy array containing all the input variables.", 'start': 2191.437, 'duration': 5.904}, {'end': 2197.762, 'text': 'So we have.', 'start': 2197.361, 'duration': 0.401}, {'end': 2200.99, 'text': 'one row for each region.', 'start': 2198.969, 'duration': 2.021}, {'end': 2204.913, 'text': 'And then we have one column for temperature, one for rainfall and one for humidity.', 'start': 2201.411, 'duration': 3.502}, {'end': 2207.194, 'text': 'And these are the exact same values that you saw earlier.', 'start': 2204.993, 'duration': 2.201}, {'end': 2212.457, 'text': 'Now, one way to convert this into floating point numbers is to just put a dot here.', 'start': 2207.975, 'duration': 4.482}, {'end': 2216.46, 'text': 'And the other way is to just specify a data type explicitly here.', 'start': 2213.178, 'duration': 3.282}], 'summary': 'Learning about numpy arrays and input variables with one row for each region and columns for temperature, rainfall, and humidity.', 'duration': 28.566, 'max_score': 2187.894, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2187894.jpg'}, {'end': 2498.352, 'src': 'heatmap', 'start': 2227.066, 'weight': 0.756, 'content': [{'end': 2229.628, 'text': 'And then we can create another numpy array for the targets.', 'start': 2227.066, 'duration': 2.562}, {'end': 2235.513, 'text': 'So once again, for the five regions, we have the yield of apples and the yield of oranges in tons per hectare.', 'start': 2229.808, 'duration': 5.705}, {'end': 2239.916, 'text': 'So that is what is captured here using this five by two matrix.', 'start': 2236.554, 'duration': 3.362}, {'end': 2243.219, 'text': 'Once again, this is a float 32 matrix.', 'start': 2241.558, 'duration': 1.661}, {'end': 2249.384, 'text': 'Now we have treated, we have separated out the inputs and the targets because we will be operating on them separately.', 'start': 2244.2, 'duration': 5.184}, {'end': 2251.666, 'text': 'Now we will be doing some matrix operations and so on.', 'start': 2249.444, 'duration': 2.222}, {'end': 2256.359, 'text': "So let's convert these arrays into PyTorch tensors.", 'start': 2253.837, 'duration': 2.522}, {'end': 2261.141, 'text': 'We say torch.fromNumpy inputs and torch.fromNumpy targets.', 'start': 2257.099, 'duration': 4.042}, {'end': 2266.184, 'text': 'Now these are PyTorch tensors.', 'start': 2264.884, 'duration': 1.3}, {'end': 2274.57, 'text': 'Next, we need to create our linear regression model to create a linear regression model.', 'start': 2270.347, 'duration': 4.223}, {'end': 2281.394, 'text': 'The first thing we need are the weights and the biases and the weights and the biases can be represented as matrices.', 'start': 2274.59, 'duration': 6.804}, {'end': 2283.015, 'text': "So let's see how.", 'start': 2282.354, 'duration': 0.661}, {'end': 2290.58, 'text': 'If you look at this expression here, you have w one, one w one, two, one, three, two, one, two, two, two, three.', 'start': 2284.318, 'duration': 6.262}, {'end': 2295.842, 'text': 'If you simply hide the temperature, rainfall, humidity, just put them away, put all the operators away for a moment.', 'start': 2290.64, 'duration': 5.202}, {'end': 2304.626, 'text': 'You can see that this kind of forms a matrix where it has two rules and it has three elements, right?', 'start': 2296.302, 'duration': 8.324}, {'end': 2308.107, 'text': 'So this is a two row by three, element by three column matrix.', 'start': 2304.666, 'duration': 3.441}, {'end': 2312.202, 'text': 'And then the biases B1 and B2, they form a vector.', 'start': 2309.761, 'duration': 2.441}, {'end': 2318.605, 'text': "So what we'll do is we will initialize the weights as a matrix, and we will initialize the biases as a vector.", 'start': 2312.923, 'duration': 5.682}, {'end': 2324.908, 'text': "And we will initialize them with random values because we don't know what good weights are.", 'start': 2320.486, 'duration': 4.422}, {'end': 2332.412, 'text': 'What are the right weights for this relationship? So we can say torch.rand n, rand n is simply going to create a.', 'start': 2325.008, 'duration': 7.404}, {'end': 2334.994, 'text': 'Torch tensor with the given shape.', 'start': 2333.513, 'duration': 1.481}, {'end': 2339.738, 'text': 'So the shape that we are passing is two comma three, two rows and three columns, a matrix.', 'start': 2335.054, 'duration': 4.684}, {'end': 2342.441, 'text': 'And we will set requires grad set to true.', 'start': 2340.599, 'duration': 1.842}, {'end': 2345.043, 'text': "And we'll see why this is useful later.", 'start': 2343.261, 'duration': 1.782}, {'end': 2348.746, 'text': 'Then we also have torched or random too.', 'start': 2346.604, 'duration': 2.142}, {'end': 2353.55, 'text': 'This is going to create a bias vector with B one B two, and this will set requires crack to true as well.', 'start': 2348.846, 'duration': 4.704}, {'end': 2355.332, 'text': "And let's print W and B.", 'start': 2354.111, 'duration': 1.221}, {'end': 2364.431, 'text': 'So torch dot random creates the tensor with a given shape and the elements are picked randomly from a normal distribution.', 'start': 2358.106, 'duration': 6.325}, {'end': 2367.613, 'text': "If you don't know what a normal distribution is, don't worry about it.", 'start': 2365.091, 'duration': 2.522}, {'end': 2374.978, 'text': 'All that means is the numbers will roughly come from the range minus one to one or minus two to two, and they will be randomly chosen.', 'start': 2367.893, 'duration': 7.085}, {'end': 2379.882, 'text': 'Now that we have the weights, our model.', 'start': 2377.44, 'duration': 2.442}, {'end': 2388.143, 'text': 'is simply a function that performs a matrix multiplication of the inputs and the weights transposed.', 'start': 2381.317, 'duration': 6.826}, {'end': 2389.804, 'text': "So let's see what that means.", 'start': 2388.863, 'duration': 0.941}, {'end': 2392.526, 'text': 'So this is one row from our input matrix.', 'start': 2390.525, 'duration': 2.001}, {'end': 2398.671, 'text': 'This is the whole input matrix and look at the first row, 73, 67, 43 temperature, rainfall, and humidity.', 'start': 2392.566, 'duration': 6.105}, {'end': 2402.995, 'text': 'Now, if we take the weights matrix and then we transpose the weights matrix.', 'start': 2399.272, 'duration': 3.723}, {'end': 2408.099, 'text': "So the first row now becomes the first column and let's just concentrate on the first column for now.", 'start': 2403.555, 'duration': 4.544}, {'end': 2416.469, 'text': 'When we perform a matrix multiplication, this element gets multiplied with this, 67 gets multiplied with w one, two,', 'start': 2409.124, 'duration': 7.345}, {'end': 2421.092, 'text': 'and 43 gets multiplied with w one, three, and together then they get added up.', 'start': 2416.469, 'duration': 4.623}, {'end': 2425.455, 'text': 'So we get 73 w one, one plus 67 w one, two plus 43 w one three.', 'start': 2421.232, 'duration': 4.223}, {'end': 2429.518, 'text': "And that's exactly what we had defined as our linear regression model.", 'start': 2426.436, 'duration': 3.082}, {'end': 2435.988, 'text': 'And then if we just add a plus here and then add a bias, so then B one also gets added to it.', 'start': 2430.805, 'duration': 5.183}, {'end': 2443.993, 'text': 'So this expression, which is, which takes a five by three matrix, multiplies it with a three by two matrix.', 'start': 2436.889, 'duration': 7.104}, {'end': 2448.336, 'text': 'So that gives us a five by two matrix, and then it adds another five by two matrix to it.', 'start': 2444.153, 'duration': 4.183}, {'end': 2453.299, 'text': 'So overall, all of this put together gives us a five row by two column matrix.', 'start': 2448.396, 'duration': 4.903}, {'end': 2462.241, 'text': "This expression will give us the predictions of the model, right? So this expression let's, let's see what that gives us.", 'start': 2454.019, 'duration': 8.222}, {'end': 2467.523, 'text': 'We take the inputs and then we say we want to do a matrix multiplication.', 'start': 2463.042, 'duration': 4.481}, {'end': 2470.644, 'text': 'So for the matrix multiplication, we use the expression at.', 'start': 2467.603, 'duration': 3.041}, {'end': 2475.486, 'text': 'the ad character represents matrix multiplication in PI torch.', 'start': 2472.165, 'duration': 3.321}, {'end': 2477.447, 'text': 'Then we call w dot T.', 'start': 2475.766, 'duration': 1.681}, {'end': 2479.367, 'text': 'So that is going to transpose the weights matrix.', 'start': 2477.447, 'duration': 1.92}, {'end': 2483.068, 'text': 'Remember we needed, we need the rows to become columns and columns to become rows.', 'start': 2479.547, 'duration': 3.521}, {'end': 2484.909, 'text': 'And then we add the bias terms.', 'start': 2483.548, 'duration': 1.361}, {'end': 2487.229, 'text': "So let's see what that gives us.", 'start': 2486.129, 'duration': 1.1}, {'end': 2488.83, 'text': 'That gives us this tensor.', 'start': 2487.629, 'duration': 1.201}, {'end': 2498.352, 'text': 'And what this tensor represents is if you look at the inputs, once again, it represents for this input for this temperature, rainfall and humidity.', 'start': 2489.73, 'duration': 8.622}], 'summary': 'Using pytorch, a linear regression model is created with weights and biases initialized as matrices and vectors, performing matrix operations to make predictions.', 'duration': 271.286, 'max_score': 2227.066, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2227066.jpg'}, {'end': 2577.952, 'src': 'embed', 'start': 2546.315, 'weight': 5, 'content': [{'end': 2551.858, 'text': 'It takes the input, performs a matrix multiplication, add some bias and churns out an output.', 'start': 2546.315, 'duration': 5.543}, {'end': 2553.779, 'text': 'This is how our model makes predictions.', 'start': 2552.239, 'duration': 1.54}, {'end': 2558.762, 'text': "Obviously it's going to be pretty bad because our weights are pretty bad, but we'll see what to do about that.", 'start': 2554.26, 'duration': 4.502}, {'end': 2561.839, 'text': "So we're just going to define it as a function.", 'start': 2560.097, 'duration': 1.742}, {'end': 2565.802, 'text': 'We are going to define a function model so that we can give it different sets of inputs.', 'start': 2561.859, 'duration': 3.943}, {'end': 2570.406, 'text': 'We can give it one input to import five inputs, new inputs and so on.', 'start': 2565.842, 'duration': 4.564}, {'end': 2577.952, 'text': 'So it takes the input matrix and it performs a matrix multiplication with the weights transposed and it adds the bias.', 'start': 2570.426, 'duration': 7.526}], 'summary': 'Model performs matrix multiplication with bias for predictions.', 'duration': 31.637, 'max_score': 2546.315, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2546315.jpg'}, {'end': 2864.823, 'src': 'embed', 'start': 2838.317, 'weight': 6, 'content': [{'end': 2845.725, 'text': 'Okay So now when we do the sum of the squares and then we take an average of that, that gives us a single number.', 'start': 2838.317, 'duration': 7.408}, {'end': 2849.269, 'text': 'So now the single number tells us how badly the model is performing.', 'start': 2845.865, 'duration': 3.404}, {'end': 2852.412, 'text': 'So this number is called the mean squared error, right?', 'start': 2849.309, 'duration': 3.103}, {'end': 2853.033, 'text': 'So we do.', 'start': 2852.452, 'duration': 0.581}, {'end': 2857.217, 'text': 'we calculate the loss between a difference between the two matrices, predictions and targets?', 'start': 2853.033, 'duration': 4.184}, {'end': 2860.841, 'text': 'Then we square all the elements of the difference matrix to remove negative values.', 'start': 2857.297, 'duration': 3.544}, {'end': 2864.823, 'text': 'And then we calculate the average of the elements in the resulting matrix.', 'start': 2861.281, 'duration': 3.542}], 'summary': 'Mean squared error measures model performance by averaging squared differences.', 'duration': 26.506, 'max_score': 2838.317, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2838317.jpg'}], 'start': 1744.722, 'title': 'Linear regression fundamentals', 'summary': 'Covers linear regression, gradient descent, and pytorch, emphasizing ease of implementation, application in crop yield prediction, and model evaluation using mean squared error.', 'chapters': [{'end': 2058.199, 'start': 1744.722, 'title': 'Linear regression and gradient descent', 'summary': "Introduces linear regression, gradient descent, and pytorch, covering foundational machine learning algorithms, pytorch's ease of implementation, and the relationship between linear regression and deep learning.", 'duration': 313.477, 'highlights': ['Linear regression is foundational in machine learning and closely related to deep learning Linear regression is foundational in machine learning and is closely related to deep learning, making it essential to understand for setting up the rest of the course.', 'Linear regression model predicts crop yields based on average temperature, rainfall, and humidity A linear regression model predicts crop yields for apples and oranges based on the average temperature, rainfall, and humidity in a region.', 'Explanation of the components of a linear regression model, including weights and bias The linear regression model involves estimating the yield of apples and oranges as a weighted sum of input variables offset by a constant, including weights for temperature, rainfall, and humidity, and a bias term.', 'Visualizing the relationship between temperature, rainfall, and yield of apples The relationship between temperature, rainfall, and the yield of apples is visualized, showing that as temperature and rainfall increase, the yield of apples also increases within reasonable values.']}, {'end': 2364.431, 'start': 2058.199, 'title': 'Linear regression for crop yield prediction', 'summary': 'Explains the learning process of linear regression to predict crop yields based on weather data, emphasizing the importance of adjusting weights using gradient descent and the conversion of input data into pytorch tensors.', 'duration': 306.232, 'highlights': ['The learning process involves figuring out a good set of weights using the training data to make accurate predictions for new data, enabling the prediction of crop yields based on weather analysis, a real-world example that happens frequently. prediction of crop yields based on weather analysis', 'The model is trained by adjusting the weights using an optimization technique called gradient descent, essential for all of deep learning. utilization of gradient descent in training the model', 'The input data, represented using NumPy arrays, consists of temperature, rainfall, and humidity values for each region, which are then converted into PyTorch tensors for further processing. representation of input data in NumPy arrays and conversion into PyTorch tensors']}, {'end': 2918.683, 'start': 2365.091, 'title': 'Linear regression model and mean squared error', 'summary': 'Explains the concept of a linear regression model through matrix multiplication and bias addition, demonstrating the process of making predictions and evaluating model performance using the mean squared error.', 'duration': 553.592, 'highlights': ['The chapter demonstrates the process of making predictions using a linear regression model, which involves matrix multiplication of the inputs and the transposed weights, along with bias addition.', "It explains the evaluation of model performance through the mean squared error, a mathematical method of comparing the models' predictions with the actual targets."]}], 'duration': 1173.961, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM1744722.jpg', 'highlights': ['Linear regression is foundational in machine learning and closely related to deep learning.', 'Linear regression model predicts crop yields based on average temperature, rainfall, and humidity.', 'The learning process involves figuring out a good set of weights using the training data to make accurate predictions for new data.', 'The model is trained by adjusting the weights using an optimization technique called gradient descent.', 'The input data, represented using NumPy arrays, consists of temperature, rainfall, and humidity values for each region, which are then converted into PyTorch tensors for further processing.', 'The chapter demonstrates the process of making predictions using a linear regression model, which involves matrix multiplication of the inputs and the transposed weights, along with bias addition.', "It explains the evaluation of model performance through the mean squared error, a mathematical method of comparing the models' predictions with the actual targets.", 'Visualizing the relationship between temperature, rainfall, and yield of apples.']}, {'end': 4092.738, 'segs': [{'end': 2948.156, 'src': 'embed', 'start': 2918.963, 'weight': 3, 'content': [{'end': 2921.165, 'text': 'You just type the code and see what happens.', 'start': 2918.963, 'duration': 2.202}, {'end': 2933.303, 'text': "So let's define the MSC loss function and we can now use the MSC loss function with the predictions and the targets to get back what is called our loss.", 'start': 2924.295, 'duration': 9.008}, {'end': 2938.547, 'text': 'Now, what does this number represent? Since this is the mean squared error.', 'start': 2934.944, 'duration': 3.603}, {'end': 2942.411, 'text': 'Now, if you work backwards, what that means is, on average,', 'start': 2938.848, 'duration': 3.563}, {'end': 2948.156, 'text': 'each element in the prediction differs from the actual target by the square root of this number.', 'start': 2942.411, 'duration': 5.745}], 'summary': 'Defining msc loss function to calculate mean squared error for predictions.', 'duration': 29.193, 'max_score': 2918.963, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2918963.jpg'}, {'end': 3081.742, 'src': 'heatmap', 'start': 2948.296, 'weight': 0.718, 'content': [{'end': 2952.699, 'text': 'So the square root of 3, 600 is about 60.', 'start': 2948.296, 'duration': 4.403}, {'end': 2955.4, 'text': 'And if that is the average difference, that is pretty bad,', 'start': 2952.699, 'duration': 2.701}, {'end': 2959.762, 'text': "considering that the numbers that we're trying to predict are themselves in the range of 50 to 200..", 'start': 2955.4, 'duration': 4.362}, {'end': 2965.465, 'text': "So the expected yield or the real yield is 60 and you're predicting 120.", 'start': 2959.762, 'duration': 5.703}, {'end': 2968.506, 'text': "That's pretty bad that you're going to lose a lot of money with that model.", 'start': 2965.465, 'duration': 3.041}, {'end': 2977.195, 'text': 'Okay And that is why this result is called a loss because it indicates how bad the model is at predicting the target variables.', 'start': 2970.051, 'duration': 7.144}, {'end': 2980.637, 'text': 'It indicates the, it represents the information loss in the model.', 'start': 2977.355, 'duration': 3.282}, {'end': 2983.458, 'text': 'So the lower the loss, the better the model.', 'start': 2981.137, 'duration': 2.321}, {'end': 2993.884, 'text': 'Okay So now we have a loss and.', 'start': 2989.982, 'duration': 3.902}, {'end': 2996.688, 'text': 'Now we need to improve the model.', 'start': 2995.427, 'duration': 1.261}, {'end': 2997.788, 'text': 'We need to reduce the loss.', 'start': 2996.728, 'duration': 1.06}, {'end': 3002.09, 'text': "And as I've said, right from the beginning, gradients play an important role here.", 'start': 2998.368, 'duration': 3.722}, {'end': 3009.413, 'text': 'And that is why, if we scroll back up to the point where we defined our weights and biases, remember,', 'start': 3002.87, 'duration': 6.543}, {'end': 3011.554, 'text': "these are the things that we've randomly initialized.", 'start': 3009.413, 'duration': 2.141}, {'end': 3013.395, 'text': 'And these are the things that we need to change.', 'start': 3011.614, 'duration': 1.781}, {'end': 3016.916, 'text': 'So that is why we need to set requires grad equals to true here.', 'start': 3013.795, 'duration': 3.121}, {'end': 3018.977, 'text': "So now we've said requires grad to true.", 'start': 3017.316, 'duration': 1.661}, {'end': 3022.819, 'text': 'And if you recall what this means is that you can go back.', 'start': 3019.277, 'duration': 3.542}, {'end': 3031.687, 'text': 'Uh, is that you can now run loss dot backward, because loss is obtained by doing a mean squared error on predictions and targets.', 'start': 3023.699, 'duration': 7.988}, {'end': 3036.852, 'text': 'predictions themselves are by are obtained by multiplying the weights matrix with the inputs and adding the bias.', 'start': 3031.687, 'duration': 5.165}, {'end': 3043.719, 'text': 'So the loss is ultimately a function of weights and biases, and of course the inputs and targets as well.', 'start': 3037.313, 'duration': 6.406}, {'end': 3046.22, 'text': 'But the inputs and targets are fixed.', 'start': 3044.76, 'duration': 1.46}, {'end': 3047.741, 'text': "We don't really want to change them.", 'start': 3046.32, 'duration': 1.421}, {'end': 3050.541, 'text': "So the weights are what's important, what we're going to change.", 'start': 3048.281, 'duration': 2.26}, {'end': 3053.382, 'text': 'So the loss is a function of the weights and the biases.', 'start': 3050.581, 'duration': 2.801}, {'end': 3060.404, 'text': 'So when we run loss dot backward, because we have said requires grad to true in the weights and in the biases.', 'start': 3053.442, 'duration': 6.962}, {'end': 3066.125, 'text': 'if I simply print out w and w dot grad, you can see here that this is the weights matrix.', 'start': 3060.404, 'duration': 5.721}, {'end': 3072.547, 'text': 'And w dot grad now contains a matrix of the same shape, but with different values.', 'start': 3067.358, 'duration': 5.189}, {'end': 3081.742, 'text': 'So what do these values represent? This value represents the derivative of the loss with respect to this weight element.', 'start': 3073.067, 'duration': 8.675}], 'summary': "The model's loss is high at 60, indicating poor predictions, leading to the need to improve the model by reducing the loss through adjusting gradients and weights.", 'duration': 133.446, 'max_score': 2948.296, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2948296.jpg'}, {'end': 3123.449, 'src': 'embed', 'start': 3095.637, 'weight': 2, 'content': [{'end': 3099.498, 'text': 'Instead of using matrix multiplication, we would have had to define all of these relationships.', 'start': 3095.637, 'duration': 3.861}, {'end': 3104.559, 'text': 'So ultimately it is loss is a function of each of these individual weights.', 'start': 3099.998, 'duration': 4.561}, {'end': 3111.045, 'text': 'And what this represents is the derivative of the loss with respect to this specific weight element.', 'start': 3105.583, 'duration': 5.462}, {'end': 3115.326, 'text': "And this is also called the partial derivative and so on, but let's not worry about that.", 'start': 3111.505, 'duration': 3.821}, {'end': 3123.449, 'text': 'Similarly, the, this element represents the derivative of the loss with respect to this specific weight element and so on.', 'start': 3116.006, 'duration': 7.443}], 'summary': 'Loss is a function of individual weights, represented by derivatives.', 'duration': 27.812, 'max_score': 3095.637, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM3095637.jpg'}, {'end': 3475.375, 'src': 'heatmap', 'start': 3401.213, 'weight': 0.701, 'content': [{'end': 3409.012, 'text': 'And it has the derivative, the derivative of the loss with respect to w one one is minus 4, 252.', 'start': 3401.213, 'duration': 7.799}, {'end': 3409.734, 'text': "That's negative.", 'start': 3409.013, 'duration': 0.721}, {'end': 3414.298, 'text': 'That means the rate of changes decreasing.', 'start': 3410.334, 'duration': 3.964}, {'end': 3419.223, 'text': 'So if what we need to do is we need to slightly increase the weight element.', 'start': 3414.438, 'duration': 4.785}, {'end': 3424.287, 'text': 'Similarly, if the derivative was positive, we would need to slightly decrease the weight element.', 'start': 3420.384, 'duration': 3.903}, {'end': 3427.09, 'text': "So there's a simple trick of doing this.", 'start': 3425.188, 'duration': 1.902}, {'end': 3430.994, 'text': 'What we do is we simply subtract the gradient.', 'start': 3427.831, 'duration': 3.163}, {'end': 3433.19, 'text': 'from the actual weight.', 'start': 3431.969, 'duration': 1.221}, {'end': 3435.512, 'text': 'So we take 0.2761.', 'start': 3433.57, 'duration': 1.942}, {'end': 3437.133, 'text': 'And from that, we simply subtract this.', 'start': 3435.512, 'duration': 1.621}, {'end': 3443.138, 'text': 'So what happens is if the, if the gradient is positive, then the weight element decreases.', 'start': 3437.534, 'duration': 5.604}, {'end': 3448.222, 'text': 'And if the gradient is negative, then the weight element increases because negative of negative becomes positive.', 'start': 3443.498, 'duration': 4.724}, {'end': 3450.744, 'text': "And you are actually adding when you're subtracting a negative number.", 'start': 3448.282, 'duration': 2.462}, {'end': 3458.291, 'text': 'Okay So now we know that we subtract the gradient to increase or to decrease or increase the weight, depending on whether it is positive or negative.', 'start': 3451.245, 'duration': 7.046}, {'end': 3466.97, 'text': 'So subtracting the gradient will give us a new weight, which will have, which will lead to a lower loss because we are going downhill along the slope.', 'start': 3459.144, 'duration': 7.826}, {'end': 3470.992, 'text': "We are descending along the gradient and that's why it's called gradient descent.", 'start': 3467.83, 'duration': 3.162}, {'end': 3475.375, 'text': "But there's a problem here.", 'start': 3474.155, 'duration': 1.22}], 'summary': 'Using gradient descent to adjust weight for lower loss, based on positive or negative derivative.', 'duration': 74.162, 'max_score': 3401.213, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM3401213.jpg'}, {'end': 3740.44, 'src': 'embed', 'start': 3711.693, 'weight': 0, 'content': [{'end': 3713.234, 'text': 'The loss is not two eight one nine.', 'start': 3711.693, 'duration': 1.541}, {'end': 3717.838, 'text': "So we went from a loss of let's see here.", 'start': 3713.775, 'duration': 4.063}, {'end': 3721.721, 'text': 'We went from a loss of three, six, four, five, 3, 645 to a loss of 2, 819.', 'start': 3718.458, 'duration': 3.263}, {'end': 3722.281, 'text': "And that's pretty good.", 'start': 3721.721, 'duration': 0.56}, {'end': 3726.544, 'text': "We've already reduced it by 40% or so.", 'start': 3722.321, 'duration': 4.223}, {'end': 3734.316, 'text': "And that's gradient descent for you.", 'start': 3733.055, 'duration': 1.261}, {'end': 3740.44, 'text': 'What we do is we simply take the gradients and descend along the gradients by subtracting a small quantity proportional to the gradient.', 'start': 3734.576, 'duration': 5.864}], 'summary': 'Reduced loss from 3,645 to 2,819, a 40% decrease using gradient descent.', 'duration': 28.747, 'max_score': 3711.693, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM3711693.jpg'}, {'end': 4024.986, 'src': 'embed', 'start': 3992.813, 'weight': 1, 'content': [{'end': 3993.193, 'text': 'There you go.', 'start': 3992.813, 'duration': 0.38}, {'end': 3999.418, 'text': 'The predictions are 62 67, which is quite close to 56 and 70.', 'start': 3993.514, 'duration': 5.904}, {'end': 4000.138, 'text': "It's not perfect.", 'start': 3999.418, 'duration': 0.72}, {'end': 4006.663, 'text': "Maybe you can go for another a hundred epochs and see what happens, but it's getting pretty close and try running this notebook.", 'start': 4000.279, 'duration': 6.384}, {'end': 4010.326, 'text': 'Try running for as many epochs as you can and see what is the lowest loss that you can get.', 'start': 4006.703, 'duration': 3.623}, {'end': 4018.544, 'text': 'So that was linear regression and gradient descent from scratch using matrix operations.', 'start': 4014.022, 'duration': 4.522}, {'end': 4021.545, 'text': 'We have understood every single operation that went in there.', 'start': 4018.624, 'duration': 2.921}, {'end': 4024.986, 'text': 'We understood the matrix multiplications that were happening.', 'start': 4021.965, 'duration': 3.021}], 'summary': 'Predictions: 62 67 close to 56 70. linear regression, gradient descent from scratch using matrix operations.', 'duration': 32.173, 'max_score': 3992.813, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM3992813.jpg'}], 'start': 2918.963, 'title': 'Msc loss function and model improvement', 'summary': 'Introduces the msc loss function and explains how mse represents the average difference between predictions and actual targets, with an example showing an average difference of 60, and discusses the application of gradient descent in adjusting weights and biases to minimize loss, leading to a lower loss and improved predictive capability.', 'chapters': [{'end': 2965.465, 'start': 2918.963, 'title': 'Msc loss function and mean squared error', 'summary': 'Introduces the msc loss function and explains how the mean squared error (mse) represents the average difference between predictions and actual targets, with an example showing an average difference of 60, which is considered bad given the range of the actual targets being 50 to 200.', 'duration': 46.502, 'highlights': ['The mean squared error (MSE) represents the average difference between predictions and actual targets, with an example showing an average difference of 60, considered bad given the range of the actual targets being 50 to 200.', 'Using the MSC loss function with the predictions and targets allows the calculation of the loss, which provides insights into the accuracy of the predictions.', 'Defining the MSC loss function allows for its use in evaluating the predictions and targets.']}, {'end': 3614.668, 'start': 2965.465, 'title': 'Applying gradient descent in model improvement', 'summary': "Discusses the concept of gradient descent and its application in adjusting weights and biases to minimize loss in the model, aiming to improve its predictive capability, as well as the use of pytorch's no_grad function to avoid unintended gradient computation, leading to a lower loss.", 'duration': 649.203, 'highlights': ['The concept of gradient descent and its application in adjusting weights and biases to minimize loss in the model The chapter explains the concept of gradient descent and its application in adjusting weights and biases to minimize loss in the model, aiming to improve its predictive capability.', "The use of PyTorch's no_grad function to avoid unintended gradient computation, leading to a lower loss The discussion includes the use of PyTorch's no_grad function to avoid unintended gradient computation, leading to a lower loss in the model.", 'The derivative of the loss with respect to specific weight elements and its role in adjusting the weights and biases The derivative of the loss with respect to specific weight elements plays a crucial role in adjusting the weights and biases to reduce the loss in the model.']}, {'end': 4092.738, 'start': 3615.109, 'title': 'Linear regression & gradient descent', 'summary': 'Covers the implementation of gradient descent for linear regression, reducing the loss by 40%, adjusting weights and biases, and training the model for 100 epochs, resulting in a lower loss of approximately 402 and predictions closer to the targets.', 'duration': 477.629, 'highlights': ['The loss is reduced by 40% using gradient descent for linear regression. The loss decreased from 3,645 to 2,819, showcasing the effectiveness of the gradient descent in reducing the loss.', 'Training the model for 100 epochs results in a lower loss of approximately 402. By repeating the process of adjusting the weights and biases for 100 epochs, the loss is reduced to approximately 402, indicating significant improvement in model performance.', 'Predictions are closer to the targets after 100 epochs of training. The predictions are 62 and 67, much closer to the targets of 56 and 70, demonstrating the improved accuracy of the model after 100 epochs of training.']}], 'duration': 1173.775, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM2918963.jpg', 'highlights': ['The loss decreased from 3,645 to 2,819, showcasing the effectiveness of the gradient descent in reducing the loss.', 'Predictions are 62 and 67, much closer to the targets of 56 and 70, demonstrating the improved accuracy of the model after 100 epochs of training.', 'The derivative of the loss with respect to specific weight elements plays a crucial role in adjusting the weights and biases to reduce the loss in the model.', 'The mean squared error (MSE) represents the average difference between predictions and actual targets, with an example showing an average difference of 60, considered bad given the range of the actual targets being 50 to 200.']}, {'end': 4849.971, 'segs': [{'end': 4724.931, 'src': 'heatmap', 'start': 4129.645, 'weight': 0.828, 'content': [{'end': 4136.932, 'text': "Okay So moving right along, we've implemented linear regression and gradient descent using the basic tensor operations primarily to understand it.", 'start': 4129.645, 'duration': 7.287}, {'end': 4140.515, 'text': 'But because this is such a common pattern in deep learning,', 'start': 4137.272, 'duration': 3.243}, {'end': 4146.781, 'text': 'PyTorch provides several built-in functions and classes to make it easy to create and train models with just a few lines of code.', 'start': 4140.515, 'duration': 6.266}, {'end': 4153.468, 'text': "And we'll see, we'll do the exact same thing that we did, but now we are going to use the built-in functions in PyTorch.", 'start': 4147.282, 'duration': 6.186}, {'end': 4157.331, 'text': "So let's begin by importing the torch dot NN package from PyTorch.", 'start': 4154.048, 'duration': 3.283}, {'end': 4160.694, 'text': 'And this contains all the utility classes for building neural networks.', 'start': 4157.531, 'duration': 3.163}, {'end': 4162.015, 'text': "And that's a surprise.", 'start': 4161.234, 'duration': 0.781}, {'end': 4168.621, 'text': 'We are talking about linear regression, but it so happens that linear regression is actually the simplest form of a neural network.', 'start': 4162.356, 'duration': 6.265}, {'end': 4174.406, 'text': 'Okay So as before, we are going to represent the inputs and targets as matrices.', 'start': 4170.183, 'duration': 4.223}, {'end': 4181.06, 'text': "So here we have an input matrix this time, instead of five regions, we've gone with 15.", 'start': 4174.687, 'duration': 6.373}, {'end': 4182.242, 'text': 'And similarly, we have targets.', 'start': 4181.06, 'duration': 1.182}, {'end': 4187.368, 'text': 'These again, we have 15 targets for each of the 15 regions yield of apples and oranges.', 'start': 4182.923, 'duration': 4.445}, {'end': 4195.075, 'text': 'And then we have the inputs and targets are converted from matrices, which is a NumPy arrays to PyTorch tensors.', 'start': 4188.287, 'duration': 6.788}, {'end': 4198.319, 'text': 'So there you go.', 'start': 4197.818, 'duration': 0.501}, {'end': 4199.9, 'text': 'And you can look at the inputs here.', 'start': 4198.719, 'duration': 1.181}, {'end': 4202.103, 'text': 'Now we have input says PyTorch tensors.', 'start': 4199.96, 'duration': 2.143}, {'end': 4209.038, 'text': "Now we're using 15 training examples because I want to illustrate how to work with large datasets in small batches.", 'start': 4203.174, 'duration': 5.864}, {'end': 4216.502, 'text': 'What you will often find in real world datasets is you will not have five or 15, but you will have maybe thousands or tens of thousands,', 'start': 4209.578, 'duration': 6.924}, {'end': 4217.943, 'text': 'or even millions of data points.', 'start': 4216.502, 'duration': 1.441}, {'end': 4226.148, 'text': "And when you're working with millions of rows of data, it will not be possible to train all of the data model with the entire dataset at once.", 'start': 4218.383, 'duration': 7.765}, {'end': 4232.032, 'text': 'It may not fit in memory, or even if it does, it may be really slow and it may actually just slow you down.', 'start': 4226.869, 'duration': 5.163}, {'end': 4238.032, 'text': 'So what we do instead is we take the dataset and break it into batches.', 'start': 4234.571, 'duration': 3.461}, {'end': 4244.214, 'text': 'So we look at maybe five regions at once and we create three batches and we perform gradient descent with these batches.', 'start': 4238.252, 'duration': 5.962}, {'end': 4253.917, 'text': 'And that helps us train, train our models faster and fit our model training within the Ram that we have.', 'start': 4246.014, 'duration': 7.903}, {'end': 4258.278, 'text': 'So to do that, there are a couple of utilities we are going to use.', 'start': 4255.697, 'duration': 2.581}, {'end': 4260.839, 'text': 'First, we need to create a tensor dataset.', 'start': 4258.878, 'duration': 1.961}, {'end': 4269.638, 'text': 'So we will import from torch.utils.data tensor dataset, and then we will pass in the inputs and targets into tensor dataset.', 'start': 4261.636, 'duration': 8.002}, {'end': 4278.94, 'text': "And we'll put that into a train DS variable and a tensor dataset allows us to access inputs and targets rows from the inputs and targets as tuples.", 'start': 4270.298, 'duration': 8.642}, {'end': 4282.16, 'text': 'So we have 15 inputs and 15 targets.', 'start': 4279.82, 'duration': 2.34}, {'end': 4284.701, 'text': 'And if we just pass in the range zero to three.', 'start': 4282.54, 'duration': 2.161}, {'end': 4287.182, 'text': 'into tensor data and to train DS.', 'start': 4285.561, 'duration': 1.621}, {'end': 4292.346, 'text': "What that's going to give us is the first three rows of inputs and the first three rows of targets.", 'start': 4287.703, 'duration': 4.643}, {'end': 4298.071, 'text': 'So this is a very simple class that simply lets you pick a slice of the data.', 'start': 4292.366, 'duration': 5.705}, {'end': 4300.232, 'text': "It doesn't have to be zero to three.", 'start': 4299.032, 'duration': 1.2}, {'end': 4309.68, 'text': 'You can also pass a array of indices and get back a specific, get back a tuple containing some specific rows from the data.', 'start': 4300.252, 'duration': 9.428}, {'end': 4315.031, 'text': 'And the first element here returned is the input variable.', 'start': 4312.028, 'duration': 3.003}, {'end': 4317.493, 'text': 'And the second element, other targets for these input rules.', 'start': 4315.071, 'duration': 2.422}, {'end': 4326.121, 'text': 'Okay Next we will create a data loader and a data loader is what is going to split our data into batches of a predefined size.', 'start': 4317.934, 'duration': 8.187}, {'end': 4327.643, 'text': 'So we set the batch size to five.', 'start': 4326.181, 'duration': 1.462}, {'end': 4333.829, 'text': 'We think that should fit in our ramp, even 15 will, but just for demonstration, we are going to create batches of five.', 'start': 4328.163, 'duration': 5.666}, {'end': 4338.75, 'text': 'And then we put in the training dataset into it, which is a tensor dataset.', 'start': 4334.986, 'duration': 3.764}, {'end': 4342.715, 'text': 'We provide the batch size into data loader and we set shuffle to true.', 'start': 4339.291, 'duration': 3.424}, {'end': 4353.768, 'text': 'What is shuffle here? When you said shuffle to true, then the data loader before creating batches is going to create a random shuffle of the data.', 'start': 4344.177, 'duration': 9.591}, {'end': 4359.17, 'text': "And let's see how that, how that is used here.", 'start': 4356.089, 'duration': 3.081}, {'end': 4362.21, 'text': "I'm going to say for XB comma YB in train DL.", 'start': 4359.21, 'duration': 3}, {'end': 4363.831, 'text': 'And this is how you use a data loader.', 'start': 4362.27, 'duration': 1.561}, {'end': 4371.073, 'text': 'This is again a nice thing about PyTorch that the classes and the objects that PyTorch provides are very Pythonic,', 'start': 4364.011, 'duration': 7.062}, {'end': 4375.694, 'text': "in the sense that they fit in very well with the kind of Python code you're already probably used to writing.", 'start': 4371.073, 'duration': 4.621}, {'end': 4385.236, 'text': 'So just as you iterate over a list or iterate over a dictionary or any other iterable object in Python, you can iterate over a data loader.', 'start': 4376.214, 'duration': 9.022}, {'end': 4391.87, 'text': 'And the data loader gives you not individual elements or individual rules, but it gives you batches of data.', 'start': 4386.187, 'duration': 5.683}, {'end': 4394.432, 'text': 'It gives you a batch of inputs and a batch of outputs.', 'start': 4391.97, 'duration': 2.462}, {'end': 4395.772, 'text': "So let's see that.", 'start': 4395.012, 'duration': 0.76}, {'end': 4401.075, 'text': "Let's say for XP, YB in train deal, print the batch of inputs and print the batch of outputs and breakout.", 'start': 4395.892, 'duration': 5.183}, {'end': 4403.376, 'text': 'So we are just going to look at the first batch.', 'start': 4401.095, 'duration': 2.281}, {'end': 4406.298, 'text': 'If you did not have this break, all three batches would get printed.', 'start': 4403.536, 'duration': 2.762}, {'end': 4408.779, 'text': 'So here is the first batch.', 'start': 4407.699, 'duration': 1.08}, {'end': 4411.261, 'text': "And let's compare that with the inputs.", 'start': 4409.78, 'duration': 1.481}, {'end': 4415.143, 'text': 'You can see that the first batch.', 'start': 4413.602, 'duration': 1.541}, {'end': 4419.125, 'text': 'does not exactly use the first five rows of inputs.', 'start': 4416.383, 'duration': 2.742}, {'end': 4420.867, 'text': 'In fact, it has picked a random sample.', 'start': 4419.185, 'duration': 1.682}, {'end': 4428.833, 'text': "And that is where shuffle equals true comes into picture that before creating batches, it's going to pick it.", 'start': 4422.768, 'duration': 6.065}, {'end': 4432.356, 'text': "It's going to shuffle the rules and then it is going to create batches.", 'start': 4429.113, 'duration': 3.243}, {'end': 4436.699, 'text': "And each time you use it in a for loop, it's going to create a different set of rules.", 'start': 4433.016, 'duration': 3.683}, {'end': 4441.783, 'text': "So if you just observe the first row here, it's going to change each time we call train deal.", 'start': 4436.719, 'duration': 5.064}, {'end': 4443.865, 'text': 'There you go.', 'start': 4443.525, 'duration': 0.34}, {'end': 4451.707, 'text': 'Now this shuffling helps randomize the input so that the loss can be reduced faster.', 'start': 4445.922, 'duration': 5.785}, {'end': 4453.189, 'text': 'It has been found empirically.', 'start': 4451.747, 'duration': 1.442}, {'end': 4455.191, 'text': 'And even if you reason about it, it makes sense.', 'start': 4453.409, 'duration': 1.782}, {'end': 4459.374, 'text': 'The more randomization that you include, the more, the faster your model trains.', 'start': 4455.271, 'duration': 4.103}, {'end': 4463.278, 'text': "Okay So that's our data loader.", 'start': 4460.155, 'duration': 3.123}, {'end': 4464.879, 'text': 'Now we know how to get batches of data.', 'start': 4463.338, 'duration': 1.541}, {'end': 4466.201, 'text': 'Next we need to create the model.', 'start': 4464.899, 'duration': 1.302}, {'end': 4472.481, 'text': 'Now we had initialized our weights matrix and a bias vector manually with randomized values.', 'start': 4467.315, 'duration': 5.166}, {'end': 4478.907, 'text': 'And then we had defined a model function, but what we can do instead is use the NN dot linear class from PyTorch.', 'start': 4472.541, 'duration': 6.366}, {'end': 4485.575, 'text': 'So the NN dot linear, what is called a linear layer of a neural network, a teaser for what is going to come afterwards.', 'start': 4479.108, 'duration': 6.467}, {'end': 4489.279, 'text': 'A linear layer is nothing but a weights and a bias matrix.', 'start': 4486.095, 'duration': 3.184}, {'end': 4494.265, 'text': 'bundled into this object, which can also be used as a function right?', 'start': 4490.481, 'duration': 3.784}, {'end': 4500.871, 'text': 'So we create NN dot linear and we give it the number of inputs, so that we have three inputs temperature, rainfall, humidity.', 'start': 4494.305, 'duration': 6.566}, {'end': 4503.894, 'text': 'So we give it the number of inputs and we give it two outputs.', 'start': 4500.891, 'duration': 3.003}, {'end': 4507.178, 'text': 'We are going to get two outputs out of it, which is.', 'start': 4504.996, 'duration': 2.182}, {'end': 4511.109, 'text': 'yield of apples and yield of oranges.', 'start': 4509.488, 'duration': 1.621}, {'end': 4513.391, 'text': "So that's going to create our model object for us.", 'start': 4511.169, 'duration': 2.222}, {'end': 4518.756, 'text': 'And when we pass in this, uh, these numbers, it automatically creates a weight and a bias.', 'start': 4513.952, 'duration': 4.804}, {'end': 4520.557, 'text': 'So weights matrix and a bias matrix.', 'start': 4518.876, 'duration': 1.681}, {'end': 4521.358, 'text': "So let's check it out.", 'start': 4520.577, 'duration': 0.781}, {'end': 4524.961, 'text': 'So model dot weight has the exact same shape as the weights matrix.', 'start': 4522.018, 'duration': 2.943}, {'end': 4528.984, 'text': 'We had created two rows and three columns and model dot bias has two elements.', 'start': 4525.001, 'duration': 3.983}, {'end': 4532.367, 'text': 'So it is a vector and both of these have requires grad set to true.', 'start': 4529.024, 'duration': 3.343}, {'end': 4533.988, 'text': "So that's convenient.", 'start': 4533.007, 'duration': 0.981}, {'end': 4540.971, 'text': 'in just one line, we have instantiated the weights and biases with random random values and they have required scratch set to true so that,', 'start': 4533.988, 'duration': 6.983}, {'end': 4542.732, 'text': "so now you're not going to forget any of this.", 'start': 4540.971, 'duration': 1.761}, {'end': 4544.653, 'text': 'You just need to set NN dot linear.', 'start': 4542.772, 'duration': 1.881}, {'end': 4550.155, 'text': 'And NN dot linear is just one form of a PyTorch model.', 'start': 4546.754, 'duration': 3.401}, {'end': 4552.837, 'text': 'There are many other modules available.', 'start': 4550.676, 'duration': 2.161}, {'end': 4557.439, 'text': "You have NN dot convolutional, which is what something, something that we're going to see later.", 'start': 4553.217, 'duration': 4.222}, {'end': 4561.675, 'text': 'You can combine, you can have a layered structure.', 'start': 4558.894, 'duration': 2.781}, {'end': 4564.517, 'text': "You're going to have a layered model, which has multiple models inside it.", 'start': 4561.696, 'duration': 2.821}, {'end': 4570.8, 'text': "So that's why the model also has a parameters method, model dot parameters.", 'start': 4565.297, 'duration': 5.503}, {'end': 4578.464, 'text': 'And this parameter method is going to, it can be used for any model to get the list of all the weights and biases matrices present inside it.', 'start': 4571.681, 'duration': 6.783}, {'end': 4585.608, 'text': 'Now, in our linear NN dot linear model we just have one weights matrix and one bias matrix, the same thing that we saw here,', 'start': 4579.025, 'duration': 6.583}, {'end': 4587.009, 'text': "but later on we're going to see how.", 'start': 4585.608, 'duration': 1.401}, {'end': 4590.842, 'text': 'There are multiple possible parameters.', 'start': 4588.26, 'duration': 2.582}, {'end': 4594.104, 'text': 'There can be a huge list of parameters inside the model.', 'start': 4591.042, 'duration': 3.062}, {'end': 4596.145, 'text': 'Okay So this is going to be useful for us.', 'start': 4594.284, 'duration': 1.861}, {'end': 4598.307, 'text': 'So remember the model dot parameters function.', 'start': 4596.185, 'duration': 2.122}, {'end': 4605.191, 'text': 'And this model can be used to generate predictions in the exact same way as we had done before.', 'start': 4600.668, 'duration': 4.523}, {'end': 4610.555, 'text': 'So earlier we had defined a model function, which takes the inputs and it multiplies it with the weight.', 'start': 4605.271, 'duration': 5.284}, {'end': 4612.995, 'text': 'transposed and adds the bias.', 'start': 4611.533, 'duration': 1.462}, {'end': 4615.457, 'text': "That's the exact same thing we can do here.", 'start': 4613.675, 'duration': 1.782}, {'end': 4619.401, 'text': 'Pass the inputs into the model, use it as a function, and that will give you predictions.', 'start': 4615.517, 'duration': 3.884}, {'end': 4623.225, 'text': 'So here you get 16 predictions of 15 predictions from the model.', 'start': 4620.162, 'duration': 3.063}, {'end': 4626.048, 'text': "We know everything that's going on so far.", 'start': 4624.506, 'duration': 1.542}, {'end': 4629.413, 'text': 'Next we are here at the loss function.', 'start': 4627.952, 'duration': 1.461}, {'end': 4635.016, 'text': 'Now, instead of defining the mean squared error loss manually, we can use the built-in function MSE loss.', 'start': 4629.793, 'duration': 5.223}, {'end': 4638.658, 'text': 'And this is present inside the torch dot NN dot functional package.', 'start': 4635.376, 'duration': 3.282}, {'end': 4645.102, 'text': 'The NN dot functional package contains a lot of functions, especially loss functions, activation functions, and so on.', 'start': 4639.198, 'duration': 5.904}, {'end': 4650.745, 'text': 'So this is another important package and we normally import it as F.', 'start': 4645.662, 'duration': 5.083}, {'end': 4653.506, 'text': 'So here we define a loss function F dot MSE loss.', 'start': 4650.745, 'duration': 2.761}, {'end': 4659.529, 'text': 'And We are simply going to use this loss function.', 'start': 4656.028, 'duration': 3.501}, {'end': 4663.891, 'text': 'We are going to pass in the predictions, which we get from passing the inputs into the models.', 'start': 4660.349, 'duration': 3.542}, {'end': 4666.953, 'text': 'And then we pass in the targets and that is going to give us the loss.', 'start': 4664.152, 'duration': 2.801}, {'end': 4670.095, 'text': 'So now this is the loss of this model.', 'start': 4668.474, 'duration': 1.621}, {'end': 4679.2, 'text': "We know what the loss is, except that this time we've used all the inbuilt things to create the model, create the loss and also represent the data.", 'start': 4671.336, 'duration': 7.864}, {'end': 4684.679, 'text': 'Next, we can now improve the model by performing gradient descent.', 'start': 4680.675, 'duration': 4.004}, {'end': 4692.668, 'text': 'And we had performed gradient descent manually, but we can use what is called an optimizer in PyTorch to perform this,', 'start': 4685.1, 'duration': 7.568}, {'end': 4694.97, 'text': 'to perform the update of the weights and biases.', 'start': 4692.668, 'duration': 2.302}, {'end': 4699.574, 'text': 'So we are going to use the optimizer torch.optim.sgd.', 'start': 4695.831, 'duration': 3.743}, {'end': 4705.538, 'text': 'SGD stands for stochastic gradient descent, which indicates that the samples are selected in random matches instead of a group.', 'start': 4699.574, 'duration': 5.964}, {'end': 4708.58, 'text': "So that's just the name of the algorithm, stochastic gradient descent.", 'start': 4705.598, 'duration': 2.982}, {'end': 4713.043, 'text': 'And inside, internally, it performs the exact same thing that we have done,', 'start': 4709.26, 'duration': 3.783}, {'end': 4724.931, 'text': 'which is subtracting from the weights and biases a small number proportional to the gradient of those gradient of the loss with respect to those weights and biases.', 'start': 4713.043, 'duration': 11.888}], 'summary': 'Implemented linear regression and gradient descent using pytorch, and worked with large datasets in small batches for faster model training.', 'duration': 595.286, 'max_score': 4129.645, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4129645.jpg'}, {'end': 4168.621, 'src': 'embed', 'start': 4140.515, 'weight': 0, 'content': [{'end': 4146.781, 'text': 'PyTorch provides several built-in functions and classes to make it easy to create and train models with just a few lines of code.', 'start': 4140.515, 'duration': 6.266}, {'end': 4153.468, 'text': "And we'll see, we'll do the exact same thing that we did, but now we are going to use the built-in functions in PyTorch.", 'start': 4147.282, 'duration': 6.186}, {'end': 4157.331, 'text': "So let's begin by importing the torch dot NN package from PyTorch.", 'start': 4154.048, 'duration': 3.283}, {'end': 4160.694, 'text': 'And this contains all the utility classes for building neural networks.', 'start': 4157.531, 'duration': 3.163}, {'end': 4162.015, 'text': "And that's a surprise.", 'start': 4161.234, 'duration': 0.781}, {'end': 4168.621, 'text': 'We are talking about linear regression, but it so happens that linear regression is actually the simplest form of a neural network.', 'start': 4162.356, 'duration': 6.265}], 'summary': 'Pytorch offers easy model creation and training with built-in functions and classes.', 'duration': 28.106, 'max_score': 4140.515, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4140515.jpg'}, {'end': 4232.032, 'src': 'embed', 'start': 4203.174, 'weight': 1, 'content': [{'end': 4209.038, 'text': "Now we're using 15 training examples because I want to illustrate how to work with large datasets in small batches.", 'start': 4203.174, 'duration': 5.864}, {'end': 4216.502, 'text': 'What you will often find in real world datasets is you will not have five or 15, but you will have maybe thousands or tens of thousands,', 'start': 4209.578, 'duration': 6.924}, {'end': 4217.943, 'text': 'or even millions of data points.', 'start': 4216.502, 'duration': 1.441}, {'end': 4226.148, 'text': "And when you're working with millions of rows of data, it will not be possible to train all of the data model with the entire dataset at once.", 'start': 4218.383, 'duration': 7.765}, {'end': 4232.032, 'text': 'It may not fit in memory, or even if it does, it may be really slow and it may actually just slow you down.', 'start': 4226.869, 'duration': 5.163}], 'summary': 'Illustrating working with large datasets in small batches, using 15 training examples.', 'duration': 28.858, 'max_score': 4203.174, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4203174.jpg'}, {'end': 4287.182, 'src': 'embed', 'start': 4261.636, 'weight': 2, 'content': [{'end': 4269.638, 'text': 'So we will import from torch.utils.data tensor dataset, and then we will pass in the inputs and targets into tensor dataset.', 'start': 4261.636, 'duration': 8.002}, {'end': 4278.94, 'text': "And we'll put that into a train DS variable and a tensor dataset allows us to access inputs and targets rows from the inputs and targets as tuples.", 'start': 4270.298, 'duration': 8.642}, {'end': 4282.16, 'text': 'So we have 15 inputs and 15 targets.', 'start': 4279.82, 'duration': 2.34}, {'end': 4284.701, 'text': 'And if we just pass in the range zero to three.', 'start': 4282.54, 'duration': 2.161}, {'end': 4287.182, 'text': 'into tensor data and to train DS.', 'start': 4285.561, 'duration': 1.621}], 'summary': 'Imported 15 inputs and targets into a tensor dataset for training.', 'duration': 25.546, 'max_score': 4261.636, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4261636.jpg'}, {'end': 4478.907, 'src': 'embed', 'start': 4436.719, 'weight': 3, 'content': [{'end': 4441.783, 'text': "So if you just observe the first row here, it's going to change each time we call train deal.", 'start': 4436.719, 'duration': 5.064}, {'end': 4443.865, 'text': 'There you go.', 'start': 4443.525, 'duration': 0.34}, {'end': 4451.707, 'text': 'Now this shuffling helps randomize the input so that the loss can be reduced faster.', 'start': 4445.922, 'duration': 5.785}, {'end': 4453.189, 'text': 'It has been found empirically.', 'start': 4451.747, 'duration': 1.442}, {'end': 4455.191, 'text': 'And even if you reason about it, it makes sense.', 'start': 4453.409, 'duration': 1.782}, {'end': 4459.374, 'text': 'The more randomization that you include, the more, the faster your model trains.', 'start': 4455.271, 'duration': 4.103}, {'end': 4463.278, 'text': "Okay So that's our data loader.", 'start': 4460.155, 'duration': 3.123}, {'end': 4464.879, 'text': 'Now we know how to get batches of data.', 'start': 4463.338, 'duration': 1.541}, {'end': 4466.201, 'text': 'Next we need to create the model.', 'start': 4464.899, 'duration': 1.302}, {'end': 4472.481, 'text': 'Now we had initialized our weights matrix and a bias vector manually with randomized values.', 'start': 4467.315, 'duration': 5.166}, {'end': 4478.907, 'text': 'And then we had defined a model function, but what we can do instead is use the NN dot linear class from PyTorch.', 'start': 4472.541, 'duration': 6.366}], 'summary': 'Shuffling input data reduces loss, speeds up model training.', 'duration': 42.188, 'max_score': 4436.719, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4436719.jpg'}, {'end': 4650.745, 'src': 'embed', 'start': 4620.162, 'weight': 5, 'content': [{'end': 4623.225, 'text': 'So here you get 16 predictions of 15 predictions from the model.', 'start': 4620.162, 'duration': 3.063}, {'end': 4626.048, 'text': "We know everything that's going on so far.", 'start': 4624.506, 'duration': 1.542}, {'end': 4629.413, 'text': 'Next we are here at the loss function.', 'start': 4627.952, 'duration': 1.461}, {'end': 4635.016, 'text': 'Now, instead of defining the mean squared error loss manually, we can use the built-in function MSE loss.', 'start': 4629.793, 'duration': 5.223}, {'end': 4638.658, 'text': 'And this is present inside the torch dot NN dot functional package.', 'start': 4635.376, 'duration': 3.282}, {'end': 4645.102, 'text': 'The NN dot functional package contains a lot of functions, especially loss functions, activation functions, and so on.', 'start': 4639.198, 'duration': 5.904}, {'end': 4650.745, 'text': 'So this is another important package and we normally import it as F.', 'start': 4645.662, 'duration': 5.083}], 'summary': 'The model generates 16 predictions, uses mse loss from torch.nn.functional package, and imports it as f.', 'duration': 30.583, 'max_score': 4620.162, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4620162.jpg'}], 'start': 4092.818, 'title': 'Pytorch for model training', 'summary': 'Covers pytorch implementation of linear regression, gradient descent, and model training, emphasizing ease of gradient computation, gpu support, batch processing, and efficient built-in functions for model creation and training.', 'chapters': [{'end': 4391.87, 'start': 4092.818, 'title': 'Pytorch linear regression and gradient descent', 'summary': 'Discusses the implementation of linear regression and gradient descent using pytorch, highlighting the ease of gradient computation, support for gpus, and utilization of built-in functions for creating and training models. it also covers the importance of working with large datasets in small batches, along with the creation of a tensor dataset and a data loader for batch processing.', 'duration': 299.052, 'highlights': ['PyTorch provides several built-in functions and classes to make it easy to create and train models with just a few lines of code PyTorch offers built-in functions and classes for effortless model creation and training, reducing the complexity and code length required.', 'The importance of working with large datasets in small batches is emphasized, with the example of using 15 training examples to illustrate the concept The significance of processing large datasets in small batches is underscored, with the example of utilizing 15 training examples to demonstrate the concept.', "The creation of a tensor dataset and a data loader for batch processing is explained, highlighting the use of 'tensor dataset' to access inputs and targets as tuples and the 'data loader' to split data into batches of a predefined size The process of creating a tensor dataset and a data loader for batch processing is elaborated, showcasing the 'tensor dataset' for accessing inputs and targets as tuples and the 'data loader' for splitting data into predefined batches."]}, {'end': 4849.971, 'start': 4391.97, 'title': 'Pytorch model training', 'summary': 'Explains how to create a pytorch model for training, including data batching, model initialization, loss function definition, and gradient descent optimization, emphasizing the importance of randomization and the use of built-in functions for efficiency.', 'duration': 458.001, 'highlights': ['Data Batching and Randomization The chapter emphasizes the importance of randomization in data batching, stating that shuffling the input data before creating batches helps reduce loss empirically and logically, making the model train faster.', "Model Initialization Using NN.linear Class It explains the use of PyTorch's NN.linear class to initialize model weights and biases with random values, providing a concise and efficient way to create a model object with the required grad set to true.", 'Loss Function and Optimization The transcript highlights the use of built-in functions like MSE loss for defining the loss function and torch.optim.sgd for performing stochastic gradient descent, showcasing the efficiency of using these pre-built functions for model improvement.']}], 'duration': 757.153, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4092818.jpg', 'highlights': ['PyTorch offers built-in functions and classes for effortless model creation and training, reducing the complexity and code length required.', 'The significance of processing large datasets in small batches is underscored, with the example of utilizing 15 training examples to demonstrate the concept.', "The process of creating a tensor dataset and a data loader for batch processing is elaborated, showcasing the 'tensor dataset' for accessing inputs and targets as tuples and the 'data loader' for splitting data into predefined batches.", 'The chapter emphasizes the importance of randomization in data batching, stating that shuffling the input data before creating batches helps reduce loss empirically and logically, making the model train faster.', "It explains the use of PyTorch's NN.linear class to initialize model weights and biases with random values, providing a concise and efficient way to create a model object with the required grad set to true.", 'The transcript highlights the use of built-in functions like MSE loss for defining the loss function and torch.optim.sgd for performing stochastic gradient descent, showcasing the efficiency of using these pre-built functions for model improvement.']}, {'end': 6542.644, 'segs': [{'end': 4964.384, 'src': 'embed', 'start': 4918.254, 'weight': 2, 'content': [{'end': 4923.675, 'text': 'And then we said, okay, what can we do to improve the weight so that the error becomes lower? The loss becomes lower.', 'start': 4918.254, 'duration': 5.421}, {'end': 4926.696, 'text': 'Gradients We can use gradients for each weight.', 'start': 4924.415, 'duration': 2.281}, {'end': 4930.917, 'text': 'We can simply move down along the gradient to reduce the loss slightly.', 'start': 4926.756, 'duration': 4.161}, {'end': 4934.357, 'text': 'So we did that for each weight and together it had a big, big effect.', 'start': 4931.297, 'duration': 3.06}, {'end': 4939.299, 'text': "And then we said, maybe let's repeat that a hundred times and see what we get.", 'start': 4935.378, 'duration': 3.921}, {'end': 4943.359, 'text': 'So we took a hundred small steps along each of those loss curves.', 'start': 4939.619, 'duration': 3.74}, {'end': 4945.8, 'text': 'And that led us to a low loss of.', 'start': 4943.839, 'duration': 1.961}, {'end': 4952.57, 'text': 'What is that 23? And that led us to this model, which gives us such accurate results.', 'start': 4947.028, 'duration': 5.542}, {'end': 4957.893, 'text': 'And indeed these productions are close to the, to the targets.', 'start': 4954.671, 'duration': 3.222}, {'end': 4962.282, 'text': "And we've trained a reasonably good model to predict crop yields for apples and oranges.", 'start': 4958.939, 'duration': 3.343}, {'end': 4964.384, 'text': 'So now we can go to a sixth region,', 'start': 4962.342, 'duration': 2.042}], 'summary': 'Using gradients, took 100 steps, achieved low loss of 23, trained a model to predict crop yields for apples and oranges.', 'duration': 46.13, 'max_score': 4918.254, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4918254.jpg'}, {'end': 5008.46, 'src': 'embed', 'start': 4982.22, 'weight': 5, 'content': [{'end': 4987.544, 'text': 'So we just create a fake batch of just one input row and create a tensor out of it.', 'start': 4982.22, 'duration': 5.324}, {'end': 4990.186, 'text': 'So now we know what to do with this batch of inputs.', 'start': 4988.184, 'duration': 2.002}, {'end': 4994.809, 'text': 'We simply put that batch of inputs into the model and we get back a batch of outputs.', 'start': 4990.326, 'duration': 4.483}, {'end': 5008.46, 'text': 'So our model now predicts that in the sixth region we are going to have 53.6 tons per hectare of apples being produced and 68.5 tons of hectare of oranges being produced.', 'start': 4995.75, 'duration': 12.71}], 'summary': 'Using a neural network model, the prediction is 53.6 tons per hectare of apples and 68.5 tons per hectare of oranges in the sixth region.', 'duration': 26.24, 'max_score': 4982.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4982220.jpg'}, {'end': 5224.606, 'src': 'embed', 'start': 5187.768, 'weight': 6, 'content': [{'end': 5191.79, 'text': 'that uses matrix operations and nonlinear activation functions and gradient descent,', 'start': 5187.768, 'duration': 4.022}, {'end': 5195.092, 'text': 'and all of these things that we are learning about to build and train models.', 'start': 5191.79, 'duration': 3.302}, {'end': 5200.715, 'text': "And it's a really powerful technique that has such widespread applicability in so many different areas.", 'start': 5195.212, 'duration': 5.503}, {'end': 5204.179, 'text': 'So Andre Carpati, the director of AI at Tesla motors.', 'start': 5201.195, 'duration': 2.984}, {'end': 5209.225, 'text': 'he has written a great blog post on this topic called software 2.2 and how artificial intelligence,', 'start': 5204.179, 'duration': 5.046}, {'end': 5216.273, 'text': 'machine learning and deep learning are completely transforming how we build software and what are all the new possibilities that they enable.', 'start': 5209.225, 'duration': 7.048}, {'end': 5219.137, 'text': 'So do check out this blog post for sure.', 'start': 5216.714, 'duration': 2.423}, {'end': 5222.445, 'text': 'So keep this picture in mind.', 'start': 5221.345, 'duration': 1.1}, {'end': 5224.606, 'text': 'Just let me hold onto it for a second.', 'start': 5222.745, 'duration': 1.861}], 'summary': 'Deep learning is a powerful technique with widespread applicability, as highlighted by andre carpati, director of ai at tesla motors.', 'duration': 36.838, 'max_score': 5187.768, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5187768.jpg'}, {'end': 5273.316, 'src': 'embed', 'start': 5244.59, 'weight': 8, 'content': [{'end': 5249.831, 'text': 'We just call Jovian or commit once again on zero to linear regression, and that has recorded a new version of the notebook.', 'start': 5244.59, 'duration': 5.241}, {'end': 5257.866, 'text': 'And you can open up the notebook on Jovian on your Jovian profile and access it there.', 'start': 5254.164, 'duration': 3.702}, {'end': 5264.53, 'text': 'One thing that you can also see as you record multiple versions is you can see visually what are the differences between your versions?', 'start': 5258.707, 'duration': 5.823}, {'end': 5266.151, 'text': 'Let me quickly show you that.', 'start': 5265.071, 'duration': 1.08}, {'end': 5273.316, 'text': 'What you can do is go to the notebook page and you can see the versions here.', 'start': 5267.852, 'duration': 5.464}], 'summary': 'Jovian has recorded a new version of the notebook, allowing users to visually compare differences between versions.', 'duration': 28.726, 'max_score': 5244.59, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5244590.jpg'}, {'end': 5441.989, 'src': 'embed', 'start': 5416.71, 'weight': 9, 'content': [{'end': 5421.439, 'text': 'And the objective of this assignment is to build is for you to build a solid understanding of PyTorch tensors.', 'start': 5416.71, 'duration': 4.729}, {'end': 5426.509, 'text': 'So what you will do in this assignment is you will pick five interesting functions related to PyTorch.', 'start': 5421.539, 'duration': 4.97}, {'end': 5430.598, 'text': 'You need to go through, you need to go through this page here.', 'start': 5427.455, 'duration': 3.143}, {'end': 5433.801, 'text': 'This talks about all the functions available in the torch module.', 'start': 5430.898, 'duration': 2.903}, {'end': 5436.444, 'text': 'A lot of these functions apply directly to tensor.', 'start': 5434.221, 'duration': 2.223}, {'end': 5437.805, 'text': 'So just pick five functions.', 'start': 5436.504, 'duration': 1.301}, {'end': 5440.648, 'text': "Please don't pick the first five.", 'start': 5438.265, 'duration': 2.383}, {'end': 5441.989, 'text': 'pick five interesting ones.', 'start': 5440.648, 'duration': 1.341}], 'summary': 'Objective: understand pytorch tensors by picking 5 interesting functions from the torch module.', 'duration': 25.279, 'max_score': 5416.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5416710.jpg'}, {'end': 5730.586, 'src': 'embed', 'start': 5705.375, 'weight': 10, 'content': [{'end': 5711.317, 'text': 'And then over the course of the next few days, we will be performing the evaluations and to receive a pass grade in this assignment.', 'start': 5705.375, 'duration': 5.942}, {'end': 5713.158, 'text': 'So you will either receive a pass or a fail grade.', 'start': 5711.338, 'duration': 1.82}, {'end': 5717.34, 'text': 'The submitted link should be a Jovian notebook that is publicly accessible.', 'start': 5713.959, 'duration': 3.381}, {'end': 5720.241, 'text': 'The notebook should demonstrate at least five tensor functions.', 'start': 5717.84, 'duration': 2.401}, {'end': 5724.783, 'text': 'There should be at least two working examples and one failing example for each function.', 'start': 5720.721, 'duration': 4.062}, {'end': 5730.586, 'text': 'And then the Jupiter notebook should also contain proper explanations.', 'start': 5727.344, 'duration': 3.242}], 'summary': 'Evaluation will determine pass/fail based on 5 tensor functions in publicly accessible jovian notebook.', 'duration': 25.211, 'max_score': 5705.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5705375.jpg'}, {'end': 5916.72, 'src': 'embed', 'start': 5889.012, 'weight': 11, 'content': [{'end': 5897.276, 'text': 'And what you will be able to do is look through the blog posts and notebooks written by other people so that you learn not just the five tensor functions that you have created,', 'start': 5889.012, 'duration': 8.264}, {'end': 5898.016, 'text': 'but you get to learn.', 'start': 5897.276, 'duration': 0.74}, {'end': 5902.637, 'text': "Maybe a hundred other tensor functions, right? So, and that's a great way to learn.", 'start': 5899.136, 'duration': 3.501}, {'end': 5904.637, 'text': "It's a crowdsourced way of learning.", 'start': 5903.097, 'duration': 1.54}, {'end': 5910.739, 'text': 'Somebody is doing all the hard work of coming up with great examples and simple explanations, and you can just spend a minute and learn about it.', 'start': 5904.677, 'duration': 6.062}, {'end': 5916.72, 'text': 'Okay So please make use of the community here, make use of all the resources available here.', 'start': 5911.499, 'duration': 5.221}], 'summary': 'Access and learn from over a hundred tensor functions and leverage community resources for efficient learning.', 'duration': 27.708, 'max_score': 5889.012, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5889012.jpg'}, {'end': 5949.769, 'src': 'embed', 'start': 5922.222, 'weight': 12, 'content': [{'end': 5928.063, 'text': 'You can click on continue discussion and ask a question here about the assignment.', 'start': 5922.222, 'duration': 5.841}, {'end': 5934.578, 'text': "So that's the assignment.", 'start': 5933.715, 'duration': 0.863}, {'end': 5937.327, 'text': 'Now, one other thing I want to tell you about is.', 'start': 5935.661, 'duration': 1.666}, {'end': 5941.124, 'text': 'The Jovian mentorship program.', 'start': 5939.443, 'duration': 1.681}, {'end': 5946.507, 'text': 'Now you can on the certificate of accomplishment that you want for this course, that is completely free of cost.', 'start': 5941.524, 'duration': 4.983}, {'end': 5949.769, 'text': 'When you do the three assignments and the course project, you will get it.', 'start': 5946.767, 'duration': 3.002}], 'summary': 'Complete 3 assignments and a project to get a free certificate from jovian mentorship program.', 'duration': 27.547, 'max_score': 5922.222, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM5922222.jpg'}, {'end': 6104.326, 'src': 'embed', 'start': 6065.686, 'weight': 0, 'content': [{'end': 6071.388, 'text': 'We will also use tricks like regularization and data augmentation to create a state of the art model.', 'start': 6065.686, 'duration': 5.702}, {'end': 6079.59, 'text': 'So we will train a state of the art deep learning model on a huge dataset with tens of thousands of images in less than five minutes.', 'start': 6071.408, 'duration': 8.182}, {'end': 6081.311, 'text': 'So this lesson is pretty exciting.', 'start': 6079.61, 'duration': 1.701}, {'end': 6090.535, 'text': 'And finally, lesson six is where we will put everything that we have learned together to create gen generative adversarial networks,', 'start': 6082.131, 'duration': 8.404}, {'end': 6098.084, 'text': 'where we will be training models that can generate images of handwritten digits or generate images of faces.', 'start': 6090.535, 'duration': 7.549}, {'end': 6104.326, 'text': "And you will be able to apply the things you've learned in any of these lessons to create your course project.", 'start': 6098.664, 'duration': 5.662}], 'summary': 'Train state-of-the-art model on huge dataset with 10k+ images in <5 min. exciting lesson on gans & application to course project.', 'duration': 38.64, 'max_score': 6065.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM6065686.jpg'}], 'start': 4850.131, 'title': 'Deep learning course overview', 'summary': 'Covers a six-lesson deep learning course, including linear regression, logistic regression, feed forward neural networks, convolutional neural networks, advanced convolutional networks, and generative adversarial networks, aiming to train a state-of-the-art deep learning model on a huge dataset with tens of thousands of images in less than five minutes and creating generative adversarial networks to generate images of handwritten digits or faces.', 'chapters': [{'end': 5008.46, 'start': 4850.131, 'title': 'Crop yield prediction model', 'summary': 'Describes the training process of a crop yield prediction model, achieving a low loss of 23, leading to accurate predictions of crop yields for apples and oranges, with a reasonably good model trained to predict the yields based on temperature, rainfall, and humidity.', 'duration': 158.329, 'highlights': ['The model achieved a low loss of 23, resulting in accurate predictions of crop yields for apples and oranges.', "The training process involved iteratively adjusting the model's weights using gradients to reduce the loss, which had a significant effect, leading to a reasonably good model.", 'The model successfully predicted crop yields for apples and oranges based on temperature, rainfall, and humidity, demonstrating the effectiveness of the training process.', "The process involved creating a fake batch of inputs to predict crop yields for a new region, showcasing the model's ability to make predictions for different scenarios."]}, {'end': 5588.9, 'start': 5009.551, 'title': 'Difference between machine learning and classical programming', 'summary': 'Explains the difference between machine learning and classical programming, emphasizing the use of models to define relationships and learn parameters from data to make predictions, and also introduces the concept of deep learning as a powerful technique with widespread applicability in various domains.', 'duration': 579.349, 'highlights': ['The chapter emphasizes the use of models to define relationships and learn parameters from data to make predictions, differentiating machine learning from classical programming. Machine learning involves using models to define relationships and learn parameters from data to make predictions, in contrast to classical programming where rules are applied to inputs to generate outputs.', 'Deep learning is introduced as a powerful technique with widespread applicability in various domains, utilizing matrix operations, nonlinear activation functions, and gradient descent. Deep learning, a branch of machine learning, is highlighted as powerful and widely applicable, leveraging matrix operations, nonlinear activation functions, and gradient descent to build and train models.', "The director of AI at Tesla Motors is mentioned to have written a blog post titled 'Software 2.2', exploring how artificial intelligence, machine learning, and deep learning are transforming software development. The director of AI at Tesla Motors is referenced for writing a blog post titled 'Software 2.2', discussing the transformative impact of artificial intelligence, machine learning, and deep learning on software development.", 'The concept of recording multiple versions of the notebook using the Jovian library and visualizing differences between versions is explained. The process of recording multiple versions of the notebook using the Jovian library and visualizing differences between versions is elaborated, providing a way to track changes and visualize them.', 'The assignment for building a solid understanding of PyTorch tensors is introduced, involving the selection and explanation of interesting PyTorch functions. An assignment focused on building a solid understanding of PyTorch tensors is introduced, requiring the selection and explanation of interesting PyTorch functions to demonstrate comprehension.']}, {'end': 5983.932, 'start': 5589.781, 'title': 'Pytorch tensor functions assignment', 'summary': 'Emphasizes the importance of committing jupyter notebooks, creating five tensor functions with three examples each, and sharing findings through a public jovian notebook and a potential blog post; also, it highlights the significance of presentation skills, community sharing, and the jovian mentorship program.', 'duration': 394.151, 'highlights': ['You need to write five functions and three examples for each function, and then write a small conclusion about what you covered in this notebook. The assignment specifies creating five functions with three examples for each, emphasizing the need for a comprehensive coverage of tensor operations in the notebook.', 'The submitted link should be a Jovian notebook that is publicly accessible. The notebook should demonstrate at least five tensor functions. The requirement for a publicly accessible Jovian notebook demonstrating at least five tensor functions is essential for the assignment submission.', 'The chapter emphasizes the importance of committing Jupyter notebooks, creating five tensor functions with three examples each, and sharing findings through a public Jovian notebook and a potential blog post. The chapter highlights the importance of committing Jupyter notebooks, creating tensor functions with examples, and sharing findings through public Jovian notebooks and potential blog posts.', 'It highlights the significance of presentation skills, community sharing, and the Jovian mentorship program. The importance of presentation skills, community sharing, and the Jovian mentorship program is underscored in the chapter.']}, {'end': 6542.644, 'start': 5984.893, 'title': 'Deep learning course overview', 'summary': 'Covers a six-lesson deep learning course, including topics such as linear regression, logistic regression, feed forward neural networks, convolutional neural networks, advanced convolutional networks, and generative adversarial networks, with a focus on image data. the course aims to train a state-of-the-art deep learning model on a huge dataset with tens of thousands of images in less than five minutes, and concludes with creating generative adversarial networks to generate images of handwritten digits or faces.', 'duration': 557.751, 'highlights': ['The course covers topics such as linear regression, logistic regression, feed forward neural networks, convolutional neural networks, advanced convolutional networks, and generative adversarial networks. The course offers a comprehensive curriculum covering various deep learning topics.', 'Training a state-of-the-art deep learning model on a huge dataset with tens of thousands of images in less than five minutes. The aim of the course is to train a high-performing deep learning model on a large image dataset in a short time frame, demonstrating the efficiency and effectiveness of the techniques taught.', 'Creating generative adversarial networks to generate images of handwritten digits or faces. The course concludes with a practical application, where students will create generative adversarial networks to generate images of handwritten digits or faces, showcasing the practical skills acquired throughout the course.']}], 'duration': 1692.513, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/5ioMqzMRFgM/pics/5ioMqzMRFgM4850131.jpg', 'highlights': ['The aim of the course is to train a high-performing deep learning model on a large image dataset in a short time frame, demonstrating the efficiency and effectiveness of the techniques taught.', 'The course concludes with a practical application, where students will create generative adversarial networks to generate images of handwritten digits or faces, showcasing the practical skills acquired throughout the course.', 'The model achieved a low loss of 23, resulting in accurate predictions of crop yields for apples and oranges.', "The training process involved iteratively adjusting the model's weights using gradients to reduce the loss, which had a significant effect, leading to a reasonably good model.", 'The model successfully predicted crop yields for apples and oranges based on temperature, rainfall, and humidity, demonstrating the effectiveness of the training process.', "The process involved creating a fake batch of inputs to predict crop yields for a new region, showcasing the model's ability to make predictions for different scenarios.", 'Deep learning, a branch of machine learning, is highlighted as powerful and widely applicable, leveraging matrix operations, nonlinear activation functions, and gradient descent to build and train models.', "The director of AI at Tesla Motors is referenced for writing a blog post titled 'Software 2.2', discussing the transformative impact of artificial intelligence, machine learning, and deep learning on software development.", 'The process of recording multiple versions of the notebook using the Jovian library and visualizing differences between versions is elaborated, providing a way to track changes and visualize them.', 'An assignment focused on building a solid understanding of PyTorch tensors is introduced, requiring the selection and explanation of interesting PyTorch functions to demonstrate comprehension.', 'The requirement for a publicly accessible Jovian notebook demonstrating at least five tensor functions is essential for the assignment submission.', 'The chapter highlights the importance of committing Jupyter notebooks, creating tensor functions with examples, and sharing findings through public Jovian notebooks and potential blog posts.', 'The importance of presentation skills, community sharing, and the Jovian mentorship program is underscored in the chapter.', 'The course offers a comprehensive curriculum covering various deep learning topics.']}], 'highlights': ['The course offers a comprehensive curriculum covering various deep learning topics.', 'The 6-week program teaches deep learning using PyTorch, with the ability to train a model to produce images of handwritten digits and anime faces.', 'PyTorch provides significant benefits in terms of Autograd, enabling automatic computation of gradients for tensor operations, essential for deep learning, and GPU support, allowing efficient processing of massive datasets and large models using GPUs, significantly reducing computation time.', 'The torch module offers a wide range of tensor operations, including functions for creating and manipulating tensors, with close to a thousand operations available.', 'Linear regression is foundational in machine learning and closely related to deep learning.', 'The loss decreased from 3,645 to 2,819, showcasing the effectiveness of the gradient descent in reducing the loss.', 'PyTorch offers built-in functions and classes for effortless model creation and training, reducing the complexity and code length required.', 'The aim of the course is to train a high-performing deep learning model on a large image dataset in a short time frame, demonstrating the efficiency and effectiveness of the techniques taught.']}