title

How to Do Linear Regression using Gradient Descent

description

The point of this is to demonstrate the concept of gradient descent. Gradient descent is the most popular optimization strategy in deep learning, in particular an implementation of it called backpropagation. We are using gradient descent as our optimization strategy for linear regression. We'll draw the line of best fit to measure the relationship between student test scores and the amount of hours studied.
Code for this video:
https://github.com/llSourcell/linear_regression_live
Yes, I've done this video before. But I'm doing it again because
1. Gradient Descent is really important. Know how it works.
2. Last time was in Google Hangouts (Ghetto) this is better quality
More learning resources:
https://spin.atomicobject.com/2014/06/24/gradient-descent-linear-regression/
https://en.wikipedia.org/wiki/Gradient_descent
http://machinelearningmastery.com/gradient-descent-for-machine-learning/
https://www.analyticsvidhya.com/blog/2017/03/introduction-to-gradient-descent-algorithm-along-its-variants/
http://ufldl.stanford.edu/tutorial/supervised/OptimizationStochasticGradientDescent/
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detail

{'title': 'How to Do Linear Regression using Gradient Descent', 'heatmap': [{'end': 768.791, 'start': 669.193, 'weight': 0.922}, {'end': 2978.265, 'start': 2884.463, 'weight': 0.716}], 'summary': "Tutorial on 'how to do linear regression using gradient descent' offers a comprehensive understanding of linear regression, gradient descent, and optimizing techniques, including insights into machine learning, data parsing, learning rate, neural network error computation, and the application of gradient descent in machine learning, with a focus on updating values to minimize errors and find the optimal solution.", 'chapters': [{'end': 228.041, 'segs': [{'end': 228.041, 'src': 'embed', 'start': 133.68, 'weight': 0, 'content': [{'end': 139.804, 'text': "we want to find the line of best fit, so that, using this line, we can then predict what a student's test score will be,", 'start': 133.68, 'duration': 6.124}, {'end': 142.725, 'text': 'given the amount of hours studied, or vice versa.', 'start': 139.804, 'duration': 2.921}, {'end': 144.246, 'text': 'but how do we get that line of best fit?', 'start': 142.725, 'duration': 1.521}, {'end': 147.248, 'text': "well, we're going to use gradient descent to do that,", 'start': 144.246, 'duration': 3.002}, {'end': 153.131, 'text': 'And so this is just a visualization of what the gradient descent process will look like to get there.', 'start': 147.888, 'duration': 5.243}, {'end': 157.493, 'text': 'And gradient descent is used everywhere in machine learning and in deep learning.', 'start': 153.211, 'duration': 4.282}, {'end': 165.037, 'text': 'We think about everything in terms of optimization, where we have some loss function that we want to minimize over time.', 'start': 158.294, 'duration': 6.743}, {'end': 168.119, 'text': 'And gradient descent is the technique we use to do that.', 'start': 165.398, 'duration': 2.721}, {'end': 170.34, 'text': "And so we're gonna talk about that.", 'start': 169.18, 'duration': 1.16}, {'end': 176.784, 'text': "So let me start off by answering, I'll do a two minutes worth of Q&A for questions, and then we'll get right into the code.", 'start': 170.36, 'duration': 6.424}, {'end': 180.027, 'text': "so, hey guys, how's it going?", 'start': 177.344, 'duration': 2.683}, {'end': 183.71, 'text': "okay, we've got some any questions.", 'start': 180.027, 'duration': 3.683}, {'end': 196.482, 'text': "questions about deep learning, ai, machine learning, uh, waiting for the rap, we'll see, was yes, questions,", 'start': 183.71, 'duration': 12.772}, {'end': 201.347, 'text': 'questions really are the the great thing to have in the beginning.', 'start': 196.482, 'duration': 4.865}, {'end': 206.622, 'text': 'Do AMA with other people?', 'start': 203.82, 'duration': 2.802}, {'end': 206.942, 'text': 'Okay,', 'start': 206.702, 'duration': 0.24}, {'end': 211.466, 'text': "So I'll give it 10 more seconds.", 'start': 207.743, 'duration': 3.723}, {'end': 214.368, 'text': '10, nine, eight, seven.', 'start': 211.486, 'duration': 2.882}, {'end': 215.789, 'text': "We're about to get into this.", 'start': 214.829, 'duration': 0.96}, {'end': 217.17, 'text': 'We are about to get into this.', 'start': 216.109, 'duration': 1.061}, {'end': 218.912, 'text': "Once I start, there's no stopping.", 'start': 217.511, 'duration': 1.401}, {'end': 219.792, 'text': "I'm like an Oreo.", 'start': 218.952, 'duration': 0.84}, {'end': 220.833, 'text': 'That really has no..', 'start': 220.113, 'duration': 0.72}, {'end': 223.155, 'text': 'relationship to what I just said.', 'start': 222.094, 'duration': 1.061}, {'end': 225.678, 'text': 'Can we do Q&A after the end of the session? Yes we can.', 'start': 223.195, 'duration': 2.483}, {'end': 228.041, 'text': "So we're gonna do Q&A every 15 minutes.", 'start': 226.039, 'duration': 2.002}], 'summary': 'Using gradient descent to find the line of best fit for predicting test scores based on hours studied in machine learning and deep learning.', 'duration': 94.361, 'max_score': 133.68, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk133680.jpg'}], 'start': 39.821, 'title': 'Linear regression, gradient descent, and q&a in ml', 'summary': 'Delves into the application of linear regression and gradient descent in analyzing the relationship between student test scores and study hours, providing insights into machine learning techniques and includes an interactive q&a and code session.', 'chapters': [{'end': 107.723, 'start': 39.821, 'title': 'Linear regression and gradient descent', 'summary': 'Discusses the relationship between student test scores and hours studied, aiming to prove it mathematically using linear regression and gradient descent optimization method, providing insights into machine learning techniques and their practical implementation.', 'duration': 67.902, 'highlights': ['Linear regression used to find relationship between student test scores and hours studied The chapter focuses on using linear regression to find the relationship between student test scores and the amount of hours studied.', 'Introduction to gradient descent for optimization The concept of gradient descent is introduced as a popular optimization method for linear regression, offering insights into machine learning techniques.', 'Practical implementation of linear regression and gradient descent The chapter covers both the conceptual and mathematical/programmatic implementation of linear regression and gradient descent, providing practical insights into the techniques.']}, {'end': 170.34, 'start': 107.723, 'title': 'Gradient descent in machine learning', 'summary': 'Explains the concept of gradient descent and its significance in machine learning and deep learning, highlighting its use in finding the line of best fit for predicting student test scores based on hours studied.', 'duration': 62.617, 'highlights': ['The chapter discusses the visualization of finding the line of best fit using gradient descent and its application in predicting student test scores based on hours studied.', 'Gradient descent is a fundamental technique in machine learning and deep learning, used for optimizing loss functions to minimize error over time.']}, {'end': 228.041, 'start': 170.36, 'title': 'Interactive q&a and code session', 'summary': 'Begins with a brief interactive q&a session on topics like deep learning, ai, and machine learning, followed by a commitment to conduct q&a every 15 minutes during the code session.', 'duration': 57.681, 'highlights': ['The chapter begins with a brief interactive Q&A session on topics like deep learning, AI, and machine learning, setting the tone for the upcoming code session.', 'The commitment to conduct Q&A every 15 minutes during the code session ensures regular interaction and engagement with the audience.']}], 'duration': 188.22, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk39821.jpg', 'highlights': ['Linear regression used to find relationship between student test scores and hours studied', 'Practical implementation of linear regression and gradient descent', 'Introduction to gradient descent for optimization', 'The chapter discusses the visualization of finding the line of best fit using gradient descent and its application in predicting student test scores based on hours studied', 'Gradient descent is a fundamental technique in machine learning and deep learning, used for optimizing loss functions to minimize error over time', 'The chapter begins with a brief interactive Q&A session on topics like deep learning, AI, and machine learning, setting the tone for the upcoming code session', 'The commitment to conduct Q&A every 15 minutes during the code session ensures regular interaction and engagement with the audience']}, {'end': 482.363, 'segs': [{'end': 285.34, 'src': 'embed', 'start': 251.202, 'weight': 1, 'content': [{'end': 252.142, 'text': "It's machine learning.", 'start': 251.202, 'duration': 0.94}, {'end': 253.443, 'text': 'There is no neural network.', 'start': 252.182, 'duration': 1.261}, {'end': 262.668, 'text': "But the reason I'm doing this is to demonstrate gradient descent because you're going to use this in almost every neural net that you built.", 'start': 253.763, 'duration': 8.905}, {'end': 266.17, 'text': "You're going to use this all over the place, okay? So that's it for the questions.", 'start': 263.068, 'duration': 3.102}, {'end': 267.07, 'text': 'Save your questions.', 'start': 266.37, 'duration': 0.7}, {'end': 272.193, 'text': "Every 15 minutes, I'm going to be answering questions, and this live stream will be an hour long, more or less.", 'start': 267.19, 'duration': 5.003}, {'end': 278.016, 'text': "So let's get started with the code, shall we? And thanks to Udacity for hosting this, by the way.", 'start': 272.793, 'duration': 5.223}, {'end': 279.157, 'text': 'Here we go.', 'start': 278.777, 'duration': 0.38}, {'end': 285.34, 'text': "So to do this, let's look at our data first.", 'start': 281.178, 'duration': 4.162}], 'summary': 'Demonstrating gradient descent, essential for neural networks. live stream scheduled for an hour with q&a every 15 minutes.', 'duration': 34.138, 'max_score': 251.202, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk251202.jpg'}, {'end': 318.554, 'src': 'embed', 'start': 290.845, 'weight': 0, 'content': [{'end': 294.447, 'text': 'So the data set is a collection of test scores and amount of hours studied.', 'start': 290.845, 'duration': 3.602}, {'end': 299.991, 'text': 'So the x values are on the left hand side, and those are the amount of hours studied.', 'start': 294.467, 'duration': 5.524}, {'end': 305.489, 'text': 'So right here, 53, 61, These are the amount of our study.', 'start': 300.151, 'duration': 5.338}, {'end': 307.41, 'text': 'And the Y values are the test scores.', 'start': 305.55, 'duration': 1.86}, {'end': 309.271, 'text': "Let's prove this relationship.", 'start': 307.73, 'duration': 1.541}, {'end': 310.111, 'text': "That's our data set.", 'start': 309.351, 'duration': 0.76}, {'end': 311.892, 'text': "It's in a data.csv file.", 'start': 310.191, 'duration': 1.701}, {'end': 313.813, 'text': 'You can find it on my GitHub.', 'start': 312.272, 'duration': 1.541}, {'end': 315.353, 'text': 'It is at the very top.', 'start': 313.833, 'duration': 1.52}, {'end': 318.554, 'text': 'It is the most recently updated code repository.', 'start': 315.833, 'duration': 2.721}], 'summary': 'Data set contains test scores and hours studied, available on github.', 'duration': 27.709, 'max_score': 290.845, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk290845.jpg'}, {'end': 441.716, 'src': 'embed', 'start': 413.434, 'weight': 2, 'content': [{'end': 422.217, 'text': "almost almost every up machine learning repository is going to import numpy in some way, and if it doesn't directly, it's gonna happen under the hood.", 'start': 413.434, 'duration': 8.783}, {'end': 427.119, 'text': 'numpy is the matrix multiplication library for machine learning.', 'start': 422.217, 'duration': 4.902}, {'end': 428.199, 'text': 'okay are?', 'start': 427.119, 'duration': 1.08}, {'end': 435.194, 'text': 'I have paid version of Sublime.', 'start': 433.934, 'duration': 1.26}, {'end': 437.115, 'text': "I'm working on a different laptop than mine.", 'start': 435.574, 'duration': 1.541}, {'end': 437.855, 'text': 'We had some issues.', 'start': 437.135, 'duration': 0.72}, {'end': 440.976, 'text': 'So I just downloaded this version of Sublime.', 'start': 437.895, 'duration': 3.081}, {'end': 441.716, 'text': "Don't worry about it.", 'start': 441.136, 'duration': 0.58}], 'summary': 'Numpy is essential for machine learning, used in almost every ml repository for matrix multiplication.', 'duration': 28.282, 'max_score': 413.434, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk413434.jpg'}], 'start': 228.381, 'title': 'Linear regression and data parsing', 'summary': 'Covers the basics of linear regression using gradient descent, with a focus on test scores and hours studied, and also delves into updating a code repository and parsing data for machine learning using python and numpy.', 'chapters': [{'end': 315.353, 'start': 228.381, 'title': 'Linear regression for test scores', 'summary': 'Covers the basics of linear regression, demonstrating the use of gradient descent, and answering questions about the method and its applications. the data set includes test scores and hours studied, with x values representing the hours studied and y values representing the test scores. the live stream is scheduled for an hour, with periodic question breaks every 15 minutes.', 'duration': 86.972, 'highlights': ["The data set comprises test scores and hours studied, with x values representing the amount of hours studied and y values representing the test scores, and is stored in a data.csv file on the speaker's GitHub.", 'Linear regression is categorized as machine learning and does not involve neural networks, the method used here for sum of squared error is commonly utilized.', 'The demonstration of gradient descent is highlighted as an essential concept for building neural networks, emphasized for its wide applicability and relevance in neural net development.']}, {'end': 482.363, 'start': 315.833, 'title': 'Code repository update and data parsing for machine learning', 'summary': 'Covers updating a code repository and parsing a data set for machine learning using python and numpy, emphasizing the importance of numpy in machine learning and the process of parsing data from a csv file with a delimiter.', 'duration': 166.53, 'highlights': ['The importance of NumPy in machine learning is emphasized, as it is mentioned that almost every machine learning repository uses NumPy for matrix multiplication. NumPy is widely used in machine learning repositories.', "The process of parsing a data set from a CSV file using NumPy's genfromtxt method with a specified delimiter (comma) is explained. Demonstration of using NumPy's genfromtxt method for data parsing.", 'The chapter begins with setting up and defining the main function for coding in Python. Initial steps for setting up and defining the main function in Python.']}], 'duration': 253.982, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk228381.jpg', 'highlights': ["The data set comprises test scores and hours studied, with x values representing the amount of hours studied and y values representing the test scores, and is stored in a data.csv file on the speaker's GitHub.", 'The demonstration of gradient descent is highlighted as an essential concept for building neural networks, emphasized for its wide applicability and relevance in neural net development.', 'The importance of NumPy in machine learning is emphasized, as it is mentioned that almost every machine learning repository uses NumPy for matrix multiplication.']}, {'end': 1148.194, 'segs': [{'end': 512.708, 'src': 'embed', 'start': 482.643, 'weight': 1, 'content': [{'end': 487.124, 'text': 'So it splits them both into a set of points, XY value pairs.', 'start': 482.643, 'duration': 4.481}, {'end': 489.025, 'text': 'So those are our points.', 'start': 487.764, 'duration': 1.261}, {'end': 494.046, 'text': 'And so the next part is the learning rate.', 'start': 490.945, 'duration': 3.101}, {'end': 503.247, 'text': 'The learning rate is going to be 0.00001, what the hell is this? So learning rate is a hyperparameter.', 'start': 494.666, 'duration': 8.581}, {'end': 512.708, 'text': 'This is a hyperparameter, and hyperparameters in machine learning are what we use as tuning knobs for our model.', 'start': 503.567, 'duration': 9.141}], 'summary': 'Learning rate set to 0.00001, a hyperparameter in machine learning.', 'duration': 30.065, 'max_score': 482.643, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk482643.jpg'}, {'end': 602.593, 'src': 'embed', 'start': 535.375, 'weight': 0, 'content': [{'end': 539.536, 'text': "and if it's too high, then the model will Sorry.", 'start': 535.375, 'duration': 4.161}, {'end': 546.278, 'text': "if the learning rate is too low, our model will be too slow to converge, whereas if it's too high, our model will never converge.", 'start': 539.536, 'duration': 6.742}, {'end': 549.078, 'text': 'So we want that balance, that optimal learning rate.', 'start': 546.578, 'duration': 2.5}, {'end': 551.699, 'text': "And in this case, it's going to be 0.001.", 'start': 549.498, 'duration': 2.201}, {'end': 558.48, 'text': "In general, in machine learning we don't always know off the top of our head what the best hyperparameters are going to be,", 'start': 551.699, 'duration': 6.781}, {'end': 559.82, 'text': 'so we have to guess and check.', 'start': 558.48, 'duration': 1.34}, {'end': 567.002, 'text': 'Now, the bleeding edge of machine learning and deep learning right now is to learn what those hyperparameters will be, okay?', 'start': 559.96, 'duration': 7.042}, {'end': 576.769, 'text': "so that's it for that part, and let's just keep going here, because we are on a roll.", 'start': 569.46, 'duration': 7.309}, {'end': 577.831, 'text': "so that's our.", 'start': 576.769, 'duration': 1.062}, {'end': 578.712, 'text': "that's it for our learning rate.", 'start': 577.831, 'duration': 0.881}, {'end': 586.201, 'text': 'and then we have our initial b value, which is going to be 0, and then we have our initial m value, which is going to be 0..', 'start': 578.712, 'duration': 7.489}, {'end': 590.825, 'text': 'what is this well this is our y equals mx plus b function.', 'start': 586.201, 'duration': 4.624}, {'end': 593.927, 'text': 'From algebra, we have the slope formula.', 'start': 591.246, 'duration': 2.681}, {'end': 598.91, 'text': 'So the b is our y intercept, and the m is our slope.', 'start': 594.107, 'duration': 4.803}, {'end': 602.593, 'text': 'It is the ideal slope that we want.', 'start': 599.171, 'duration': 3.422}], 'summary': 'Optimal learning rate is 0.001 for model convergence in machine learning.', 'duration': 67.218, 'max_score': 535.375, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk535375.jpg'}, {'end': 648.961, 'src': 'embed', 'start': 619.047, 'weight': 5, 'content': [{'end': 626.669, 'text': "So how many iterations do we want to run our training step for? So we're going to say 1,000.", 'start': 619.047, 'duration': 7.622}, {'end': 630.149, 'text': 'Why 1,000? Well, because we have such a small data set.', 'start': 626.669, 'duration': 3.48}, {'end': 637.051, 'text': 'If the data set was bigger, then we would have to do 10,000 or 100,000, start incorporating GPUs and stuff like that.', 'start': 630.209, 'duration': 6.842}, {'end': 642.697, 'text': "But we'll start off with just 1,000.", 'start': 637.091, 'duration': 5.606}, {'end': 648.961, 'text': 'There is that and now I want to print out the starting graph.', 'start': 642.697, 'duration': 6.264}], 'summary': 'Training step will run for 1,000 iterations due to small data set.', 'duration': 29.914, 'max_score': 619.047, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk619047.jpg'}, {'end': 768.791, 'src': 'heatmap', 'start': 669.193, 'weight': 0.922, 'content': [{'end': 673.218, 'text': "So this is the highest value, and then we're going to print out what BNM are.", 'start': 669.193, 'duration': 4.025}, {'end': 681.716, 'text': 'So print, print B and then print M, depending on what the, those are gonna be the optimal values.', 'start': 673.278, 'duration': 8.438}, {'end': 684.697, 'text': 'So this step is going to output the optimal values for each of them.', 'start': 681.776, 'duration': 2.921}, {'end': 688.298, 'text': "So what we're gonna feed it is we're gonna feed everything we've just defined.", 'start': 685.117, 'duration': 3.181}, {'end': 694.8, 'text': 'We defined our points, we defined that initial B value, we defined the initial M value, we defined the learning rate,', 'start': 688.738, 'duration': 6.062}, {'end': 701.318, 'text': "we define the number of iterations, we define a couple things, and so we're gonna feed this all into the model.", 'start': 695.748, 'duration': 5.57}, {'end': 705.886, 'text': "Okay, so that's it for the highest level, and okay.", 'start': 702.019, 'duration': 3.867}, {'end': 715.329, 'text': "so now what we're gonna do is we're going to get into the gradient descent runner step,", 'start': 705.886, 'duration': 9.443}, {'end': 719.092, 'text': 'so the gradient descent runner step is going to be defined now.', 'start': 715.329, 'duration': 3.763}, {'end': 729.26, 'text': "okay, so let's look at what this looks like, given our values that we define, which were the points and the starting b value and the starting m value,", 'start': 719.092, 'duration': 10.168}, {'end': 733.243, 'text': 'and the learning rate and the number of iterations.', 'start': 729.26, 'duration': 3.983}, {'end': 736.806, 'text': "okay, so let's do this.", 'start': 733.243, 'duration': 3.563}, {'end': 738.247, 'text': "okay, who's with me?", 'start': 736.806, 'duration': 1.441}, {'end': 740.628, 'text': "we've got the B values.", 'start': 738.247, 'duration': 2.381}, {'end': 750.657, 'text': 'the B value to start off with is going to be what we gave it, which is 0, and the end value, as you can guess, is also going to start off as 0.', 'start': 740.628, 'duration': 10.029}, {'end': 751.618, 'text': "They'll both start off as zero.", 'start': 750.657, 'duration': 0.961}, {'end': 752.959, 'text': "We've got to learn these things.", 'start': 751.918, 'duration': 1.041}, {'end': 754.62, 'text': 'This is machine learning.', 'start': 753.239, 'duration': 1.381}, {'end': 756.341, 'text': "It's not machine define it statically.", 'start': 754.68, 'duration': 1.661}, {'end': 768.791, 'text': "So for i in range, number of iterations, which are 1,000, we're going to say, well, let's get those values using something called the step gradient.", 'start': 756.642, 'duration': 12.149}], 'summary': 'The transcript outlines the process of optimizing values using gradient descent and defining the inputs for the model.', 'duration': 99.598, 'max_score': 669.193, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk669193.jpg'}, {'end': 970.362, 'src': 'embed', 'start': 936.491, 'weight': 3, 'content': [{'end': 937.612, 'text': 'Sometimes they call it loss.', 'start': 936.491, 'duration': 1.121}, {'end': 939.874, 'text': 'Error, loss, it means the same thing.', 'start': 937.992, 'duration': 1.882}, {'end': 941.155, 'text': 'We want to minimize error.', 'start': 940.134, 'duration': 1.021}, {'end': 942.757, 'text': 'We want to minimize loss.', 'start': 941.275, 'duration': 1.482}, {'end': 946.561, 'text': 'And the way to do that is to compute what that error is.', 'start': 943.297, 'duration': 3.264}, {'end': 949.584, 'text': 'And depending on the use case, it could be different things.', 'start': 947.061, 'duration': 2.523}, {'end': 952.247, 'text': 'In this case, simple linear regression.', 'start': 950.084, 'duration': 2.163}, {'end': 953.488, 'text': 'We have a set of points.', 'start': 952.547, 'duration': 0.941}, {'end': 956.611, 'text': 'We want to..', 'start': 953.828, 'duration': 2.783}, {'end': 957.172, 'text': 'We want to..', 'start': 956.611, 'duration': 0.561}, {'end': 960.458, 'text': 'define what that error is.', 'start': 959.298, 'duration': 1.16}, {'end': 961.319, 'text': "So here's what it is.", 'start': 960.498, 'duration': 0.821}, {'end': 964.68, 'text': 'We start off with a line and we have a set of points,', 'start': 962.039, 'duration': 2.641}, {'end': 970.362, 'text': 'and what we want to do is we want to measure the distance between each of those points at a certain time step.', 'start': 964.68, 'duration': 5.682}], 'summary': 'Minimize error in simple linear regression by computing and defining it using distance between points.', 'duration': 33.871, 'max_score': 936.491, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk936491.jpg'}], 'start': 482.643, 'title': 'Understanding learning rate in machine learning', 'summary': 'Explains the concept of learning rate as a hyperparameter in machine learning, emphasizing the importance of finding the optimal learning rate for model convergence, and the process of guessing and checking for determining hyperparameters in machine learning.', 'chapters': [{'end': 577.831, 'start': 482.643, 'title': 'Understanding learning rate in machine learning', 'summary': 'Explains the concept of learning rate as a hyperparameter in machine learning, emphasizing the importance of finding the optimal learning rate for model convergence, and the process of guessing and checking for determining hyperparameters in machine learning.', 'duration': 95.188, 'highlights': ['The learning rate is a hyperparameter used as a tuning knob for the model, and it defines how fast the model learns.', "It's crucial to find the optimal learning rate for model convergence, as a too low learning rate leads to slow convergence, while a too high learning rate prevents convergence.", 'In machine learning, determining the best hyperparameters often involves a process of guessing and checking.']}, {'end': 1148.194, 'start': 577.831, 'title': 'Linear regression and gradient descent', 'summary': "Covers the process of implementing linear regression and gradient descent for machine learning, including setting initial values, defining the number of iterations, and explaining the computation of error in the context of minimizing loss, with the goal of updating the model's prediction at each time step.", 'duration': 570.363, 'highlights': ["Explaining the computation of error in the context of minimizing loss The chapter emphasizes the importance of computing error to update the model's prediction at each time step, using the sum of squared errors to measure the distance from each point to the line, and aiming to minimize the error value.", 'Setting initial values for linear regression The initial values for the y equals mx plus b function are set as 0 for the initial b and m values, which are gradually learned over time in the context of machine learning.', 'Defining the number of iterations for gradient descent The chapter specifies the number of iterations for running the training step as 1,000, considering the small dataset, and acknowledges that for larger datasets, more iterations and advanced hardware like GPUs would be required.']}], 'duration': 665.551, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk482643.jpg', 'highlights': ["It's crucial to find the optimal learning rate for model convergence, as a too low learning rate leads to slow convergence, while a too high learning rate prevents convergence.", 'The learning rate is a hyperparameter used as a tuning knob for the model, and it defines how fast the model learns.', 'In machine learning, determining the best hyperparameters often involves a process of guessing and checking.', "Explaining the computation of error in the context of minimizing loss The chapter emphasizes the importance of computing error to update the model's prediction at each time step, using the sum of squared errors to measure the distance from each point to the line, and aiming to minimize the error value.", 'Setting initial values for linear regression The initial values for the y equals mx plus b function are set as 0 for the initial b and m values, which are gradually learned over time in the context of machine learning.', 'Defining the number of iterations for gradient descent The chapter specifies the number of iterations for running the training step as 1,000, considering the small dataset, and acknowledges that for larger datasets, more iterations and advanced hardware like GPUs would be required.']}, {'end': 1571.012, 'segs': [{'end': 1174.409, 'src': 'embed', 'start': 1148.815, 'weight': 1, 'content': [{'end': 1153.618, 'text': 'All of this is done by our interpreter, and we can just focus on what matters, which is the algorithms.', 'start': 1148.815, 'duration': 4.803}, {'end': 1157.28, 'text': "And so that's why Python is used, because we can focus on the algorithms.", 'start': 1153.978, 'duration': 3.302}, {'end': 1158.04, 'text': 'One more question.', 'start': 1157.36, 'duration': 0.68}, {'end': 1165.004, 'text': "Can you make a video about different layers in a neural network? We'll see.", 'start': 1158.48, 'duration': 6.524}, {'end': 1166.044, 'text': "We'll see.", 'start': 1165.744, 'duration': 0.3}, {'end': 1168.046, 'text': "I don't know exactly what you mean by that.", 'start': 1166.945, 'duration': 1.101}, {'end': 1168.686, 'text': "That's why I said that.", 'start': 1168.086, 'duration': 0.6}, {'end': 1170.647, 'text': "Okay, so now let's get back to computing this error.", 'start': 1168.746, 'duration': 1.901}, {'end': 1174.409, 'text': "So how do we compute this? So get ready for some math, and I'm going to explain what's happening here.", 'start': 1170.907, 'duration': 3.502}], 'summary': 'Python used for focusing on algorithms, computing neural network layers, and explaining error computation.', 'duration': 25.594, 'max_score': 1148.815, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1148815.jpg'}, {'end': 1295.769, 'src': 'embed', 'start': 1232.257, 'weight': 0, 'content': [{'end': 1234.518, 'text': 'Why? Because we are going to sum it in a second.', 'start': 1232.257, 'duration': 2.261}, {'end': 1238.619, 'text': "And two is because we don't actually care about the value itself.", 'start': 1234.918, 'duration': 3.701}, {'end': 1240.42, 'text': 'We just care about the magnitude.', 'start': 1239, 'duration': 1.42}, {'end': 1243.621, 'text': "So we're looking at it from a more abstract perspective.", 'start': 1240.94, 'duration': 2.681}, {'end': 1245.742, 'text': 'We just care about the magnitude of these values.', 'start': 1243.641, 'duration': 2.101}, {'end': 1247.723, 'text': 'We want to minimize the magnitude.', 'start': 1246.042, 'duration': 1.681}, {'end': 1253.847, 'text': "So that's going to give us the difference between our uh y intercepts.", 'start': 1248.143, 'duration': 5.704}, {'end': 1260.212, 'text': "and then a little refresher on this e that means sigma, that's a, that's a notation, for that is called sigma notation,", 'start': 1253.847, 'duration': 6.365}, {'end': 1265.316, 'text': 'and what it does is it defines a set of uh values that we want to iterate over.', 'start': 1260.212, 'duration': 5.104}, {'end': 1271.08, 'text': "so what we're saying here is for when i equals one up to n, where n is the number of points.", 'start': 1265.316, 'duration': 5.764}, {'end': 1278.804, 'text': 'so for all of those points we want to measure the squared error values for all of the points against our line,', 'start': 1271.08, 'duration': 7.724}, {'end': 1284.625, 'text': "and then 1 over n means we want to find the average of those, and that's going to give us one value.", 'start': 1278.804, 'duration': 5.821}, {'end': 1290.767, 'text': 'and that one value is the error value, the sum of the squared errors.', 'start': 1284.625, 'duration': 6.142}, {'end': 1295.769, 'text': 'and every time step we want to minimize this value using gradient descent.', 'start': 1290.767, 'duration': 5.002}], 'summary': 'Using sigma notation to minimize squared errors with gradient descent.', 'duration': 63.512, 'max_score': 1232.257, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1232257.jpg'}, {'end': 1525.882, 'src': 'embed', 'start': 1496.615, 'weight': 4, 'content': [{'end': 1498.436, 'text': "That's it for our compute error function.", 'start': 1496.615, 'duration': 1.821}, {'end': 1503.62, 'text': "So where were we? We were in that last function, and the most important function, because it's talking about gradient descent.", 'start': 1498.456, 'duration': 5.164}, {'end': 1513.046, 'text': "Listen up, because this is gonna be used almost every time when you're using deep neural networks, all the time.", 'start': 1504.821, 'duration': 8.225}, {'end': 1514.908, 'text': 'Know this like the back of your hand.', 'start': 1513.427, 'duration': 1.481}, {'end': 1516.629, 'text': "Gradient descent, let's go.", 'start': 1514.968, 'duration': 1.661}, {'end': 1519.2, 'text': "Let's go.", 'start': 1518.76, 'duration': 0.44}, {'end': 1522.101, 'text': 'Tails I got some, see, I got some of my homies in here.', 'start': 1519.86, 'duration': 2.241}, {'end': 1523.861, 'text': "Cool So let's go with this.", 'start': 1522.121, 'duration': 1.74}, {'end': 1525.882, 'text': "We've got gradient descent.", 'start': 1524.282, 'duration': 1.6}], 'summary': 'Importance of gradient descent in deep neural networks.', 'duration': 29.267, 'max_score': 1496.615, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1496615.jpg'}], 'start': 1148.815, 'title': 'Neural network error computation', 'summary': 'Delves into the computation of error in neural networks, emphasizing the importance of minimizing squared errors and explaining sigma notation for measuring errors. it also covers gradient descent and the computation of total error, crucial for deep neural networks.', 'chapters': [{'end': 1253.847, 'start': 1148.815, 'title': 'Neural network layers & error computation', 'summary': 'Explains the computation of error in a neural network, emphasizing the importance of minimizing the magnitude of squared errors by explaining the algebraic equation and its significance in optimizing y intercepts.', 'duration': 105.032, 'highlights': ['The chapter explains the algebraic equation y=mx+b and its significance in minimizing the distance between data set points and points on the line, by squaring the difference to ensure positivity and emphasizing the focus on minimizing the magnitude of the values.', 'Python is used to focus on the algorithms in neural network implementation.']}, {'end': 1374.222, 'start': 1253.847, 'title': 'Understanding sigma notation for error measurement', 'summary': 'Explains the concept of sigma notation for measuring squared errors in a dataset, aiming to find the average error value, which is crucial for the gradient descent optimization process.', 'duration': 120.375, 'highlights': ['The chapter introduces sigma notation for defining a set of values to iterate over, specifically for measuring the squared error values for all points against a line, aiming to find the average error value, crucial for the gradient descent process.', 'It explains the process of iteratively computing the error for every point in the dataset, by programmatically encoding the mathematical equation and finding x and y values for each point.']}, {'end': 1571.012, 'start': 1374.423, 'title': 'Gradient descent and computing total error', 'summary': 'Covers the computation of total error by summing squared errors, dividing by the number of points, and introduces the concept of gradient descent, crucial for deep neural networks.', 'duration': 196.589, 'highlights': ['The total error value is computed by summing the squared errors and then averaging it with the total number of points. The total error value is calculated by summing the squared errors of the predicted values and the actual values, and then averaging it with the total number of points.', 'The transcript introduces the concept of gradient descent, which is crucial for deep neural networks. The chapter explains the concept of gradient descent, emphasizing its importance for deep neural networks and its frequent usage.']}], 'duration': 422.197, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1148815.jpg', 'highlights': ['The chapter emphasizes the focus on minimizing the magnitude of the values in the algebraic equation y=mx+b.', 'Python is used to focus on the algorithms in neural network implementation.', 'The chapter introduces sigma notation for defining a set of values to iterate over, specifically for measuring the squared error values for all points against a line.', 'The total error value is computed by summing the squared errors and then averaging it with the total number of points.', 'The chapter explains the concept of gradient descent, emphasizing its importance for deep neural networks and its frequent usage.']}, {'end': 2055.578, 'segs': [{'end': 1615.74, 'src': 'embed', 'start': 1591.903, 'weight': 1, 'content': [{'end': 1601.911, 'text': 'And so if we were to map all of those triplets so we could think of them as triplets of slope, y-intercept and error value pairs,', 'start': 1591.903, 'duration': 10.008}, {'end': 1603.112, 'text': 'it would make this graph', 'start': 1601.911, 'duration': 1.201}, {'end': 1608.417, 'text': 'What we want to do is we want to find that point where the error is smallest.', 'start': 1603.733, 'duration': 4.684}, {'end': 1611.738, 'text': 'we want to find the point where the error is smallest.', 'start': 1609.397, 'duration': 2.341}, {'end': 1615.74, 'text': 'And if we look at this graph, we can see what that point is visually.', 'start': 1612.238, 'duration': 3.502}], 'summary': 'Finding the point with the smallest error on a graph of triplets.', 'duration': 23.837, 'max_score': 1591.903, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1591903.jpg'}, {'end': 1682.084, 'src': 'embed', 'start': 1639.997, 'weight': 0, 'content': [{'end': 1648.021, 'text': "Let's find that smallest point because that smallest point at the very bottom of this graph is going to give us the ideal y-intercept and slope.", 'start': 1639.997, 'duration': 8.024}, {'end': 1658.486, 'text': "So if we find the minimum error, the minimal error, the smallest error possible, we'll also get the ideal y-intercept and the ideal slope,", 'start': 1648.361, 'duration': 10.125}, {'end': 1660.027, 'text': 'the ideal b and m.', 'start': 1658.486, 'duration': 1.541}, {'end': 1665.63, 'text': 'And what do we do with those ideal b and m values? Well, we plug them into our y equals mx plus b equation.', 'start': 1660.027, 'duration': 5.603}, {'end': 1672.114, 'text': 'And what happens when we plug them into our y equals mx plus b equation? we get the line of best fit.', 'start': 1665.97, 'duration': 6.144}, {'end': 1677.66, 'text': 'Now, I want to say, by the way, this is not the optimal way of doing linear regression.', 'start': 1672.595, 'duration': 5.065}, {'end': 1682.084, 'text': 'We could compute these b and m values using simple algebra.', 'start': 1677.7, 'duration': 4.384}], 'summary': 'Finding the smallest error yields ideal y-intercept and slope for linear regression.', 'duration': 42.087, 'max_score': 1639.997, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1639997.jpg'}, {'end': 1772.397, 'src': 'embed', 'start': 1729.485, 'weight': 3, 'content': [{'end': 1731.127, 'text': 'we have a thousand iterations.', 'start': 1729.485, 'duration': 1.642}, {'end': 1737.914, 'text': 'we want to iteratively move our point where we are in space, in this three-dimensional space, down to that smallest point.', 'start': 1731.127, 'duration': 6.787}, {'end': 1741.238, 'text': 'And the way we do that is by calculating the gradient.', 'start': 1738.495, 'duration': 2.743}, {'end': 1743.54, 'text': 'And the gradient gives us a direction.', 'start': 1741.338, 'duration': 2.202}, {'end': 1745.382, 'text': 'It means slope, it means direction.', 'start': 1743.74, 'duration': 1.642}, {'end': 1747.724, 'text': 'We need to talk about this for a second.', 'start': 1746.423, 'duration': 1.301}, {'end': 1748.744, 'text': 'This is important, listen up.', 'start': 1747.744, 'duration': 1}, {'end': 1752.526, 'text': 'So gradient values are used all over the place in machine learning.', 'start': 1748.964, 'duration': 3.562}, {'end': 1757.248, 'text': "So I was talking to Ian Goodfellow, right? He's the guy who invented generative adversarial networks.", 'start': 1752.846, 'duration': 4.402}, {'end': 1765.91, 'text': "And he was saying that some problems we can't do, we can't solve because we don't have the gradient.", 'start': 1757.608, 'duration': 8.302}, {'end': 1769.374, 'text': 'So clearly gradients are used across machine learning.', 'start': 1766.15, 'duration': 3.224}, {'end': 1772.397, 'text': 'Sometimes in machine learning we call functions differentiable.', 'start': 1769.654, 'duration': 2.743}], 'summary': 'In machine learning, gradients are crucial for iterative movement and problem-solving. they are used extensively across the field, as per ian goodfellow.', 'duration': 42.912, 'max_score': 1729.485, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1729485.jpg'}], 'start': 1571.292, 'title': 'Optimizing linear regression and gradient descent in machine learning', 'summary': 'Covers optimizing linear regression to find the ideal line of best fit for machine learning, and understanding gradient descent, emphasizing its importance, application, computation of gradient values, utilizing 1000 iterations, and handling complex data with partial derivatives.', 'chapters': [{'end': 1682.084, 'start': 1571.292, 'title': 'Optimizing linear regression for best fit line', 'summary': 'Explains how to optimize linear regression to find the smallest error, y-intercept, and slope in a graph, ultimately leading to the ideal line of best fit for machine learning.', 'duration': 110.792, 'highlights': ['By mapping all possible triplets of slope, y-intercept, and error values, the goal is to find the point where the error is smallest, leading to the local minima in machine learning.', 'Finding the smallest error point at the bottom of the graph yields the ideal y-intercept and slope for the best fit line in the y=mx+b equation.', 'The process involves first order optimization to compute the ideal y-intercept and slope, which are then used to generate the line of best fit for the data.']}, {'end': 2055.578, 'start': 1682.745, 'title': 'Understanding gradient descent in machine learning', 'summary': 'Explains the concept of gradient descent in machine learning, highlighting its importance, application, and computation of gradient values, with a mention of 1000 iterations and the use of partial derivatives, in order to optimize models and handle complex data.', 'duration': 372.833, 'highlights': ['The concept of gradient descent is explained, emphasizing its importance in machine learning and its application in various problems, as mentioned by Ian Goodfellow, the inventor of generative adversarial networks.', 'The computation of gradient values and the use of partial derivatives are discussed, with a mention of the need to compute the gradient to determine the direction of movement, and the use of 1000 iterations to iteratively move to the smallest point in a three-dimensional space.', 'The explanation delves into the significance of gradient values in machine learning, particularly in determining the direction of movement, with the gradient being compared to a tangent line and its role in guiding whether to move up or down in a bowl-like curve.', 'The process is likened to a bowl, with the goal being to find the optimal point for the model by utilizing gradient descent to make decisions on whether to move up or down, thus obtaining the optimal values for the model.', 'The chapter touches upon the use of partial derivatives in calculus to calculate the gradient, particularly with respect to the values b and m, and the subsequent encoding of these equations programmatically, which is described as the most important part of gradient descent.']}], 'duration': 484.286, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk1571292.jpg', 'highlights': ['The process involves first order optimization to compute the ideal y-intercept and slope, which are then used to generate the line of best fit for the data.', 'By mapping all possible triplets of slope, y-intercept, and error values, the goal is to find the point where the error is smallest, leading to the local minima in machine learning.', 'Finding the smallest error point at the bottom of the graph yields the ideal y-intercept and slope for the best fit line in the y=mx+b equation.', 'The concept of gradient descent is explained, emphasizing its importance in machine learning and its application in various problems, as mentioned by Ian Goodfellow, the inventor of generative adversarial networks.', 'The computation of gradient values and the use of partial derivatives are discussed, with a mention of the need to compute the gradient to determine the direction of movement, and the use of 1000 iterations to iteratively move to the smallest point in a three-dimensional space.']}, {'end': 3189.948, 'segs': [{'end': 2125.95, 'src': 'embed', 'start': 2055.998, 'weight': 0, 'content': [{'end': 2066.237, 'text': "In more complex cases, there are There could be several local minima, and we want to find the ideal one, but we'll get to that.", 'start': 2055.998, 'duration': 10.239}, {'end': 2073.442, 'text': "It's just another set of, we want to learn where to do gradient descent, and that's later steps.", 'start': 2066.598, 'duration': 6.844}, {'end': 2075.663, 'text': 'One more question.', 'start': 2074.943, 'duration': 0.72}, {'end': 2082.467, 'text': 'To start off with ML and AI, do you recommend starting with the ML and D followed by, nope.', 'start': 2077.824, 'duration': 4.643}, {'end': 2084.128, 'text': 'Are there..', 'start': 2083.768, 'duration': 0.36}, {'end': 2090.755, 'text': 'Why the gradient gives the minima? That is a mathematical question, yes.', 'start': 2087.395, 'duration': 3.36}, {'end': 2098.36, 'text': "So why does the gradient give the minima? Because it doesn't give us the minima to start off with.", 'start': 2091.217, 'duration': 7.143}, {'end': 2101.421, 'text': 'The gradient just tells us how to update our value.', 'start': 2098.64, 'duration': 2.781}, {'end': 2103.502, 'text': 'Should we update them with a positive??', 'start': 2101.441, 'duration': 2.061}, {'end': 2104.942, 'text': 'Should we update them with a negative??', 'start': 2103.522, 'duration': 1.42}, {'end': 2107.203, 'text': 'Should we multiply them by?', 'start': 2105.463, 'duration': 1.74}, {'end': 2115.487, 'text': 'you know, how do we modify our values to make them closer and closer to the optimal values where the error is the smallest?', 'start': 2107.203, 'duration': 8.284}, {'end': 2119.388, 'text': "and once we're finally there, that is our minima.", 'start': 2116.187, 'duration': 3.201}, {'end': 2121.689, 'text': "okay, it doesn't directly give us a minimum.", 'start': 2119.388, 'duration': 2.301}, {'end': 2125.95, 'text': 'all it does is it gives us a direction to minimize our error or loss.', 'start': 2121.689, 'duration': 4.261}], 'summary': 'In ml and ai, the gradient guides updating values towards the optimal minimum to minimize error or loss.', 'duration': 69.952, 'max_score': 2055.998, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk2055998.jpg'}, {'end': 2622.593, 'src': 'embed', 'start': 2599.525, 'weight': 4, 'content': [{'end': 2608.629, 'text': "To do that, we're going to calculate the partial derivative with respect to B and with respect to M, which are these two respective lines right here.", 'start': 2599.525, 'duration': 9.104}, {'end': 2613.071, 'text': "Given all of our points, okay, given all of these points, we're calculating that.", 'start': 2609.329, 'duration': 3.742}, {'end': 2620.273, 'text': "We'll sum them all up and then we'll multiply it by the learning rate and subtract that from what we have currently,", 'start': 2614.391, 'duration': 5.882}, {'end': 2622.593, 'text': "and that's going to give us our new B and M values.", 'start': 2620.273, 'duration': 2.32}], 'summary': 'Calculating partial derivatives for b and m to update values.', 'duration': 23.068, 'max_score': 2599.525, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk2599525.jpg'}, {'end': 2978.265, 'src': 'heatmap', 'start': 2884.463, 'weight': 0.716, 'content': [{'end': 2899.186, 'text': "Interesting Also, this is the code that I'm reading it from.", 'start': 2884.463, 'duration': 14.723}, {'end': 2913.955, 'text': 'There it is.', 'start': 2913.595, 'duration': 0.36}, {'end': 2916.496, 'text': 'Okay, so there it is.', 'start': 2914.475, 'duration': 2.021}, {'end': 2918.536, 'text': "So I'm actually curious what the error was.", 'start': 2916.836, 'duration': 1.7}, {'end': 2930.039, 'text': "What was it? It was gen from text, but it wasn't defined because..", 'start': 2919.117, 'duration': 10.922}, {'end': 2938.422, 'text': 'Because.. Oh, I misspelled it.', 'start': 2930.039, 'duration': 8.383}, {'end': 2939.162, 'text': "That's why.", 'start': 2938.762, 'duration': 0.4}, {'end': 2940.342, 'text': 'Duh, okay.', 'start': 2939.822, 'duration': 0.52}, {'end': 2942.623, 'text': "But anyway, it's because I misspelled it.", 'start': 2940.602, 'duration': 2.021}, {'end': 2946.572, 'text': 'But yes, I misspelled the gen from text.', 'start': 2943.79, 'duration': 2.782}, {'end': 2953.498, 'text': 'So after 1,000 iterations, we get the ideal B, we get the ideal M values, both of them.', 'start': 2948.854, 'duration': 4.644}, {'end': 2961.964, 'text': "And we can plug them into our equation, and it's going to give us the optimal line of best fit for our values,", 'start': 2953.838, 'duration': 8.126}, {'end': 2967.989, 'text': 'which we can then use to make a prediction, given a test score or given a right.', 'start': 2961.964, 'duration': 6.025}, {'end': 2968.489, 'text': "that's what's up.", 'start': 2967.989, 'duration': 0.5}, {'end': 2978.265, 'text': "Yes, that's what it was, gen from TXT, not gen from T-E-X-T.", 'start': 2974.383, 'duration': 3.882}], 'summary': 'After 1,000 iterations, achieved ideal b and m values for best fit line, allowing accurate predictions.', 'duration': 93.802, 'max_score': 2884.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk2884463.jpg'}, {'end': 3085.231, 'src': 'embed', 'start': 3055.176, 'weight': 5, 'content': [{'end': 3058.097, 'text': "How do we learn to learn? So it's kind of like a meta learning.", 'start': 3055.176, 'duration': 2.921}, {'end': 3059.979, 'text': "Three more questions and then we're out of here.", 'start': 3058.558, 'duration': 1.421}, {'end': 3065.882, 'text': "Is it possible to overshoot the minimum? Yes, and so that's why we have a learning rate, so we don't overshoot it.", 'start': 3060.979, 'duration': 4.903}, {'end': 3070.164, 'text': 'For larger data sets, yes, we can absolutely overshoot our minimum.', 'start': 3067.382, 'duration': 2.782}, {'end': 3073.505, 'text': 'Our model could just be training forever and never converge.', 'start': 3070.664, 'duration': 2.841}, {'end': 3080.549, 'text': 'Two more, are you going to implement LSTM from scratch? Yeah, I actually have a video where I do that.', 'start': 3074.646, 'duration': 5.903}, {'end': 3085.231, 'text': 'It is generate Wikipedia articles, check that out.', 'start': 3080.709, 'duration': 4.522}], 'summary': 'Meta-learning explores overshooting and lstm implementation for model training.', 'duration': 30.055, 'max_score': 3055.176, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk3055176.jpg'}, {'end': 3163.844, 'src': 'embed', 'start': 3131.71, 'weight': 3, 'content': [{'end': 3135.054, 'text': 'Is the feedback related to backpropagation? This is not backpropagation.', 'start': 3131.71, 'duration': 3.344}, {'end': 3139.579, 'text': 'So backpropagation is a form of gradient descent.', 'start': 3135.074, 'duration': 4.505}, {'end': 3141.861, 'text': 'This is not backpropagation.', 'start': 3140.28, 'duration': 1.581}, {'end': 3145.105, 'text': 'This is gradient descent for linear regression.', 'start': 3142.342, 'duration': 2.763}, {'end': 3151.732, 'text': "If we take gradient descent and apply it to deep neural networks, that's when it becomes backpropagation.", 'start': 3145.425, 'duration': 6.307}, {'end': 3154.715, 'text': 'so gradient descent is the big, is the big boy.', 'start': 3152.273, 'duration': 2.442}, {'end': 3163.844, 'text': 'and then back propagation is an implementation of gradient descent where we are descending our gradient by propagating an error backwards across all of our layers.', 'start': 3154.715, 'duration': 9.129}], 'summary': 'Backpropagation is a form of gradient descent used in deep neural networks.', 'duration': 32.134, 'max_score': 3131.71, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk3131710.jpg'}], 'start': 2055.998, 'title': 'Gradient descent in ml', 'summary': 'Explores gradient descent in machine learning, covering the process of updating values to minimize error and find the optimal solution, as well as the implementation for linear regression, addressing questions about dynamic learning rates, overshooting the minimum, scalability, and differentiation from backpropagation.', 'chapters': [{'end': 2125.95, 'start': 2055.998, 'title': 'Understanding gradient descent in ml', 'summary': 'Explores the concept of gradient descent in machine learning, emphasizing the process of updating values to minimize error and find the optimal solution, while addressing the mathematical basis behind the gradient and its role in reaching the minimum.', 'duration': 69.952, 'highlights': ['The gradient provides the direction to minimize error or loss, helping to update values to approach the optimal solution.', 'In complex cases, there could be several local minima, and the goal is to find the ideal one, which is determined through the process of gradient descent.', 'Understanding the mathematical basis of why the gradient gives the minima is crucial, as it guides the process of updating values to approach the optimal solution.']}, {'end': 3189.948, 'start': 2125.95, 'title': 'Implementing gradient descent for optimization', 'summary': 'Demonstrates the implementation of gradient descent for linear regression, explaining the calculation of gradients, updating of parameters using learning rate, and addressing questions about dynamic learning rates, overshooting the minimum, scalability, and differentiation from backpropagation.', 'duration': 1063.998, 'highlights': ['The chapter demonstrates the implementation of gradient descent for linear regression. The focus of the chapter is on implementing the gradient descent algorithm for optimizing a linear regression model.', 'Explaining the calculation of gradients and updating of parameters using learning rate. The chapter details the process of calculating gradients for the parameters B and M, and then updating these parameters using a learning rate in the context of linear regression.', 'Addressing questions about dynamic learning rates, overshooting the minimum, scalability, and differentiation from backpropagation. The presenter addresses various questions from the audience, including the potential use of dynamic learning rates, preventing overshooting of the minimum, scalability of the technique for large datasets, and differentiating gradient descent from backpropagation.']}], 'duration': 1133.95, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/XdM6ER7zTLk/pics/XdM6ER7zTLk2055998.jpg', 'highlights': ['The gradient provides the direction to minimize error or loss, helping to update values to approach the optimal solution.', 'Understanding the mathematical basis of why the gradient gives the minima is crucial, as it guides the process of updating values to approach the optimal solution.', 'In complex cases, there could be several local minima, and the goal is to find the ideal one, which is determined through the process of gradient descent.', 'The chapter demonstrates the implementation of gradient descent for linear regression. The focus of the chapter is on implementing the gradient descent algorithm for optimizing a linear regression model.', 'Explaining the calculation of gradients and updating of parameters using learning rate. The chapter details the process of calculating gradients for the parameters B and M, and then updating these parameters using a learning rate in the context of linear regression.', 'Addressing questions about dynamic learning rates, overshooting the minimum, scalability, and differentiation from backpropagation. The presenter addresses various questions from the audience, including the potential use of dynamic learning rates, preventing overshooting of the minimum, scalability of the technique for large datasets, and differentiating gradient descent from backpropagation.']}], 'highlights': ['The chapter emphasizes the focus on minimizing the magnitude of the values in the algebraic equation y=mx+b.', 'The process involves first order optimization to compute the ideal y-intercept and slope, which are then used to generate the line of best fit for the data.', 'Understanding the mathematical basis of why the gradient gives the minima is crucial, as it guides the process of updating values to approach the optimal solution.', 'The commitment to conduct Q&A every 15 minutes during the code session ensures regular interaction and engagement with the audience', "The data set comprises test scores and hours studied, with x values representing the amount of hours studied and y values representing the test scores, and is stored in a data.csv file on the speaker's GitHub.", 'The chapter discusses the visualization of finding the line of best fit using gradient descent and its application in predicting student test scores based on hours studied', 'The chapter introduces sigma notation for defining a set of values to iterate over, specifically for measuring the squared error values for all points against a line.', 'The chapter explains the concept of gradient descent, emphasizing its importance for deep neural networks and its frequent usage.', 'The gradient provides the direction to minimize error or loss, helping to update values to approach the optimal solution.', 'Explaining the calculation of gradients and updating of parameters using learning rate. The chapter details the process of calculating gradients for the parameters B and M, and then updating these parameters using a learning rate in the context of linear regression.']}