title
What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial for Beginners | Simplilearn

description
🔥Professional Certificate Course In AI And Machine Learning by IIT Kanpur (India Only): https://www.simplilearn.com/iitk-professional-certificate-course-ai-machine-learning?utm_campaign=23AugustTubebuddyExpPCPAIandML&utm_medium=DescriptionFF&utm_source=youtube 🔥AI Engineer Masters Program (Discount Code - YTBE15): https://www.simplilearn.com/masters-in-artificial-intelligence?utm_campaign=SCE-AIMasters&utm_medium=DescriptionFF&utm_source=youtube 🔥AI & Machine Learning Bootcamp(US Only): https://www.simplilearn.com/ai-machine-learning-bootcamp?utm_campaign=WhatisTF-E8n_k6HNAgs&utm_medium=Descriptionff&utm_source=youtube 🔥 Purdue Post Graduate Program In AI And Machine Learning: https://www.simplilearn.com/pgp-ai-machine-learning-certification-training-course?utm_campaign=WhatisTF-E8n_k6HNAgs&utm_medium=Descriptionff&utm_source=youtube This TensorFlow tutorial will help you in understanding what is TensorFlow and how it is used in Deep Learning. In this tutorial, you will learn the fundamentals of TensorFlow, why TensorFlow, what are Tenors, what is a data flow graph, top deep learning libraries along a use case implementation using TensorFlow. Below topics are explained in this TensorFlow Tutorial for beginners: 1. What is Deep Learning? 0:02:00 2. Top Deep Learning Libraries 0:05:04 3. Why TensorFlow? 0:06:25 4. What is TensorFlow? 0:08:37 5. What are Tensors? 0:10:05 6. What is a Data Flow Graph? 0:12:34 7. Program Elements in TensorFlow 0:15:09 8. Use case implementation using TensorFlow 0:22:58 To learn more about TensorFlow and Deep Learning, subscribe to our YouTube channel: https://www.youtube.com/user/Simplilearn?sub_confirmation=1 You can also go through the slides here: https://goo.gl/5krhus Watch more videos on Deep Learning: https://www.youtube.com/playlist?list=PLEiEAq2VkUUIYQ-mMRAGilfOKyWKpHSip TensorFlow installation Tutorial: https://www.youtube.com/watch?v=Ejzubp-B83o&t=710s #TensorFlowTutorialForBeginners #TensorFlow #TensorFlowTutorial #WhatIsTensorFlow #TensorFlowForBeginners #IntroductionToTensorFlow #LearnTensorFlow #PythonTensorFlowTutorial #DeepLearningTutorial #DeepLearningWithTensorFlow #SimplilearnDeepLearning #Simplilearn ➡️ About Post Graduate Program In AI And Machine Learning This AI ML course is designed to enhance your career in AI and ML by demystifying concepts like machine learning, deep learning, NLP, computer vision, reinforcement learning, and more. You'll also have access to 4 live sessions, led by industry experts, covering the latest advancements in AI such as generative modeling, ChatGPT, OpenAI, and chatbots. ✅ Key Features - Post Graduate Program certificate and Alumni Association membership - Exclusive hackathons and Ask me Anything sessions by IBM - 3 Capstones and 25+ Projects with industry data sets from Twitter, Uber, Mercedes Benz, and many more - Master Classes delivered by Purdue faculty and IBM experts - Simplilearn's JobAssist helps you get noticed by top hiring companies - Gain access to 4 live online sessions on latest AI trends such as ChatGPT, generative AI, explainable AI, and more - Learn about the applications of ChatGPT, OpenAI, Dall-E, Midjourney & other prominent tools ✅ Skills Covered - ChatGPT - Generative AI - Explainable AI - Generative Modeling - Statistics - Python - Supervised Learning - Unsupervised Learning - NLP - Neural Networks - Computer Vision - And Many More… 👉 Learn More At: Learn more at: https://www.simplilearn.com/deep-learning-course-with-tensorflow-training?utm_campaign=What-is-TensorFlow-E8n_k6HNAgs&utm_medium=Tutorials&utm_source=youtube 🔥Free AI Course: https://www.simplilearn.com/learn-ai-basics-skillup?utm_campaign=WhatisTF&utm_medium=Description&utm_source=youtube 🔥🔥 Interested in Attending Live Classes? Call Us: IN - 18002127688 / US - +18445327688

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{'title': 'What is TensorFlow? | Introduction to TensorFlow | TensorFlow Tutorial for Beginners | Simplilearn', 'heatmap': [], 'summary': 'Provides an in-depth introduction to tensorflow, covering basics, deep learning, tensorflow programming, graph building, sessions, model training, and classification, with a focus on libraries, components, and practical implementations.', 'chapters': [{'end': 287.141, 'segs': [{'end': 62.442, 'src': 'embed', 'start': 3.442, 'weight': 0, 'content': [{'end': 6.463, 'text': 'Hello and welcome to this session on what is TensorFlow.', 'start': 3.442, 'duration': 3.021}, {'end': 9.605, 'text': "Today we're going to see what is deep learning, very briefly,", 'start': 6.603, 'duration': 3.002}, {'end': 16.248, 'text': 'and then we will talk about what are the various libraries that are available for developing deep learning applications,', 'start': 9.605, 'duration': 6.643}, {'end': 22.071, 'text': 'and then we will focus on TensorFlow and what are the various advantages, why we should use TensorFlow.', 'start': 16.248, 'duration': 5.823}, {'end': 23.231, 'text': 'what is TensorFlow?', 'start': 22.071, 'duration': 1.16}, {'end': 26.533, 'text': 'and then we will talk about what are tensors, which are as the name suggests.', 'start': 23.231, 'duration': 3.302}, {'end': 33.518, 'text': 'TensorFlow consists of tensors and then they are executed in a certain graphical format of flow, like a graph,', 'start': 26.533, 'duration': 6.985}, {'end': 40.825, 'text': "and that's why it is named as such tensorflow, and we will see how to write programs in tensorflow.", 'start': 33.518, 'duration': 7.307}, {'end': 43.427, 'text': 'the programming language, of course, will be python,', 'start': 40.825, 'duration': 2.602}, {'end': 48.913, 'text': 'but there is a certain way in which you write deep learning application or a program in tensorflow.', 'start': 43.427, 'duration': 5.486}, {'end': 57.919, 'text': 'So we will take a look at that, this basic introductory session for beginners, and then we will see one implementation of TensorFlow code,', 'start': 49.073, 'duration': 8.846}, {'end': 62.442, 'text': "one full end-to-end, and that's pretty much it what you can expect from this.", 'start': 57.919, 'duration': 4.523}], 'summary': 'Intro to tensorflow, its advantages, and programming in python for deep learning applications.', 'duration': 59, 'max_score': 3.442, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3442.jpg'}, {'end': 121.317, 'src': 'embed', 'start': 98.119, 'weight': 2, 'content': [{'end': 108.166, 'text': 'so in case you are very new to machine learning and python everything, then it may be a good idea to check other tutorials that we have uploaded on,', 'start': 98.119, 'duration': 10.047}, {'end': 114.992, 'text': "let's say about python and also about machine learning, so that you get a basic understanding and then come back here.", 'start': 108.166, 'duration': 6.826}, {'end': 119.436, 'text': 'otherwise it may be very difficult to understand this tutorial Alright.', 'start': 114.992, 'duration': 4.444}, {'end': 121.317, 'text': "so, having said that, let's get started.", 'start': 119.436, 'duration': 1.881}], 'summary': 'New to machine learning? check our python and ml tutorials for basic understanding before proceeding.', 'duration': 23.198, 'max_score': 98.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs98119.jpg'}, {'end': 188.44, 'src': 'embed', 'start': 143.368, 'weight': 1, 'content': [{'end': 148.232, 'text': 'And the underlying technology behind artificial intelligence is deep learning.', 'start': 143.368, 'duration': 4.864}, {'end': 153.917, 'text': "And here we teach them how to recognize, let's say, images or voice and so on and so forth.", 'start': 148.552, 'duration': 5.365}, {'end': 156.258, 'text': 'So it is a learning mechanism.', 'start': 153.957, 'duration': 2.301}, {'end': 163.324, 'text': 'But here, unlike traditional machine learning, the data is far more complicated and far more unstructured.', 'start': 156.519, 'duration': 6.805}, {'end': 168.848, 'text': 'Like it could be primarily in the form of images or audio files or text files.', 'start': 163.524, 'duration': 5.324}, {'end': 173.271, 'text': 'and one of the core components of deep learning is neural network,', 'start': 169.128, 'duration': 4.143}, {'end': 181.476, 'text': 'and a neural network somewhat looks like this there is something known as an input layer and then there is an output layer and in between there are a bunch of hidden layers.', 'start': 173.271, 'duration': 8.205}, {'end': 188.44, 'text': 'so typically there would be at least one hidden layer, and anything more than one hidden layer is known as a deep neural network.', 'start': 181.476, 'duration': 6.964}], 'summary': 'Deep learning uses neural networks to process complex, unstructured data like images and audio, with at least one hidden layer.', 'duration': 45.072, 'max_score': 143.368, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs143368.jpg'}], 'start': 3.442, 'title': 'Tensorflow basics and deep learning', 'summary': 'Covers the basics of tensorflow, its relation to deep learning, various libraries for deep learning, advantages of using tensorflow, and writing programs in tensorflow using python. it also introduces the prerequisites for understanding deep learning, emphasizes the relationship between deep learning and machine learning, and explains the structure and functions of neural networks.', 'chapters': [{'end': 62.442, 'start': 3.442, 'title': 'Understanding tensorflow basics', 'summary': 'Introduces the concept of tensorflow, covering its relation to deep learning, various libraries for deep learning, the advantages of using tensorflow, and a basic overview of writing programs in tensorflow using python.', 'duration': 59, 'highlights': ['The session covers the basics of TensorFlow, including its relation to deep learning, available libraries, advantages of using TensorFlow, and programming in Python.', 'TensorFlow consists of tensors executed in a graphical format, and the programming language used will be Python.', 'The chapter includes a basic introductory session for beginners and an implementation of TensorFlow code for a comprehensive understanding.']}, {'end': 287.141, 'start': 62.582, 'title': 'Introduction to deep learning', 'summary': 'Introduces the prerequisites for understanding deep learning, emphasizes the relationship between deep learning and machine learning, and explains the structure and functions of neural networks in a concise manner.', 'duration': 224.559, 'highlights': ['Deep learning is a subset of machine learning primarily using neural networks, and it serves as the underlying technology behind artificial intelligence, teaching recognition of complex data such as images, audio, and text. Deep learning is a subset of machine learning that primarily uses neural networks and serves as the underlying technology behind artificial intelligence. It teaches recognition of complex data such as images, audio, and text.', 'The tutorial emphasizes the importance of having some understanding of machine learning and Python before delving into the content, suggesting that viewers should refer to other tutorials if they are new to these concepts. The tutorial emphasizes the importance of having some understanding of machine learning and Python before delving into the content, suggesting that viewers should refer to other tutorials if they are new to these concepts.', 'The structure and function of neural networks, including the input layer, hidden layers, and output layer, are explained, with a focus on their roles in processing data and making predictions. The structure and function of neural networks, including the input layer, hidden layers, and output layer, are explained, with a focus on their roles in processing data and making predictions.']}], 'duration': 283.699, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3442.jpg', 'highlights': ['Covers basics of TensorFlow, its relation to deep learning, available libraries, advantages of using TensorFlow, and programming in Python.', 'Deep learning is a subset of machine learning primarily using neural networks, serving as the underlying technology behind artificial intelligence, teaching recognition of complex data such as images, audio, and text.', 'The tutorial emphasizes the importance of having some understanding of machine learning and Python before delving into the content, suggesting that viewers should refer to other tutorials if they are new to these concepts.', 'The session includes a basic introductory session for beginners and an implementation of TensorFlow code for a comprehensive understanding.', 'TensorFlow consists of tensors executed in a graphical format, and the programming language used will be Python.', 'The structure and function of neural networks, including the input layer, hidden layers, and output layer, are explained, with a focus on their roles in processing data and making predictions.']}, {'end': 970.09, 'segs': [{'end': 330.649, 'src': 'embed', 'start': 304.72, 'weight': 2, 'content': [{'end': 309.922, 'text': 'Primarily, there are two or three components that are required in order to develop deep learning application.', 'start': 304.72, 'duration': 5.202}, {'end': 312.003, 'text': 'You need obviously a programming language.', 'start': 310.142, 'duration': 1.861}, {'end': 316.424, 'text': "So typically Python is used and that's what we are going to use in this particular video.", 'start': 312.063, 'duration': 4.361}, {'end': 320.646, 'text': 'But you can also use other languages like Java or C++ and so on.', 'start': 316.504, 'duration': 4.142}, {'end': 327.608, 'text': 'And there are some libraries that are readily available and for primarily for doing machine learning and deep learning.', 'start': 320.786, 'duration': 6.822}, {'end': 330.649, 'text': 'programming So these are a list of libraries.', 'start': 327.728, 'duration': 2.921}], 'summary': 'Deep learning applications require programming languages like python, with the option for others like java or c++. libraries are available for machine learning and deep learning.', 'duration': 25.929, 'max_score': 304.72, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs304720.jpg'}, {'end': 433.286, 'src': 'embed', 'start': 405.832, 'weight': 0, 'content': [{'end': 409.414, 'text': 'What these libraries, like TensorFlow, offer is.', 'start': 405.832, 'duration': 3.582}, {'end': 417.398, 'text': "they provide kind of a high level API so that we don't have to go really deep into writing all the stuff that is required,", 'start': 409.414, 'duration': 7.984}, {'end': 424.201, 'text': "let's say to prepare a neural network and to even configure or even to program a neuron, and so on.", 'start': 417.398, 'duration': 6.803}, {'end': 426.603, 'text': 'So these are done by the library.', 'start': 424.301, 'duration': 2.302}, {'end': 429.804, 'text': 'So all you need to do is they offer a higher level API.', 'start': 426.883, 'duration': 2.921}, {'end': 433.286, 'text': 'You need to use that API and call that API and maybe pass the data.', 'start': 429.904, 'duration': 3.382}], 'summary': 'Libraries like tensorflow offer high-level apis for neural network preparation, configuration, and programming, reducing the need for deep coding.', 'duration': 27.454, 'max_score': 405.832, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs405832.jpg'}, {'end': 524.627, 'src': 'embed', 'start': 492.312, 'weight': 1, 'content': [{'end': 496.773, 'text': 'So GPUs, as the name suggests, graphical processing unit were originally designed for that.', 'start': 492.312, 'duration': 4.461}, {'end': 502.856, 'text': 'But since they are very good at handling this kind of iterative calculations and so on, now they are kind of.', 'start': 496.873, 'duration': 5.983}, {'end': 507.777, 'text': 'they are being used or leveraged rather for doing or developing deep learning applications.', 'start': 502.856, 'duration': 4.921}, {'end': 511.619, 'text': 'And TensorFlow supports GPUs as well as CPUs.', 'start': 508.017, 'duration': 3.602}, {'end': 515.44, 'text': "So I think that's one of the major advantages of TensorFlow as well.", 'start': 511.799, 'duration': 3.641}, {'end': 524.627, 'text': "Now, again, what is exactly TensorFlow? It's an open source library developed by Google and open source and primarily for deep learning development.", 'start': 515.6, 'duration': 9.027}], 'summary': "Gpus leveraged for deep learning. tensorflow supports gpus and cpus. it's an open source library primarily for deep learning.", 'duration': 32.315, 'max_score': 492.312, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs492312.jpg'}, {'end': 637.447, 'src': 'embed', 'start': 611.686, 'weight': 3, 'content': [{'end': 616.549, 'text': 'now, when we are doing deep learning, especially the training process, you will have large.', 'start': 611.686, 'duration': 4.863}, {'end': 627.358, 'text': 'It really helps when you are able to put this, use this or store it in a compact way,', 'start': 621.272, 'duration': 6.086}, {'end': 634.464, 'text': 'and so tensors actually offer a very nice and compact way of storing the data, handling the data during computation.', 'start': 627.358, 'duration': 7.106}, {'end': 637.447, 'text': 'This is not really for storing on your hard disk or things like that.', 'start': 634.504, 'duration': 2.943}], 'summary': 'Deep learning training benefits from compact data storage using tensors for efficient computation.', 'duration': 25.761, 'max_score': 611.686, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs611686.jpg'}, {'end': 837.988, 'src': 'embed', 'start': 808.365, 'weight': 4, 'content': [{'end': 812.808, 'text': "and when you're preparing the graph, no, none of the code is actually getting executed.", 'start': 808.365, 'duration': 4.443}, {'end': 817.091, 'text': 'you write the code to prepare the graph and then you execute that graph.', 'start': 812.808, 'duration': 4.283}, {'end': 819.193, 'text': "so that's the way, by creating a session.", 'start': 817.091, 'duration': 2.102}, {'end': 822.235, 'text': "that's the way tensorflow program works.", 'start': 819.193, 'duration': 3.042}, {'end': 831.262, 'text': 'so, and each of these computation is represented as what is known as a data flow graph and we will also see that whenever you start a tensorflow,', 'start': 822.535, 'duration': 8.727}, {'end': 837.988, 'text': 'when you create an object tensorflow object, there will be what is known as a default graph and then, if required, i know,', 'start': 831.262, 'duration': 6.726}], 'summary': 'Tensorflow program creates a data flow graph, executed in a session.', 'duration': 29.623, 'max_score': 808.365, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs808365.jpg'}], 'start': 287.342, 'title': 'Using tensorflow for deep learning', 'summary': 'Discusses the key components required to develop a deep learning application, including popular libraries such as tensorflow, keras, and theano, the benefits of using tensorflow with its high-level apis and support for various languages and hardware, and the advantages of using gpus for iterative calculations in deep learning.', 'chapters': [{'end': 454.675, 'start': 287.342, 'title': 'Tensorflow for deep learning', 'summary': 'Discusses the key components required to develop a deep learning application, including the programming language, popular libraries such as tensorflow, keras and theano, and the benefits of using tensorflow with its high-level apis and support for various languages and hardware.', 'duration': 167.333, 'highlights': ['TensorFlow offers high-level APIs for easier development of deep learning applications, supporting multiple languages and hardware. TensorFlow provides high-level APIs, simplifying the process of developing deep learning applications and supporting multiple languages such as Python, C++, Java, and even integration with R. It also supports both CPUs and GPUs, making it versatile and accessible for various hardware configurations.', 'Key components required for deep learning application development include programming language (e.g., Python), and popular libraries like TensorFlow, Keras, and Theano. The key components for developing deep learning applications involve using programming languages such as Python, and leveraging popular libraries like TensorFlow, Keras, and Theano to facilitate the development process.', 'TensorFlow simplifies the coding process by providing high-level APIs, reducing the complexity of writing code for tasks like configuring a neural network or programming a neuron. TensorFlow simplifies the coding process by offering high-level APIs, which streamlines the development of neural networks and reduces the complexity of tasks like configuring a neural network or programming a neuron.']}, {'end': 970.09, 'start': 454.735, 'title': 'Tensorflow for deep learning', 'summary': 'Explains the computational intensity of deep learning, the advantages of using gpus for iterative calculations in deep learning, the role of tensorflow as an open-source library for deep learning and its support for traditional machine learning, the use of tensors for data storage and handling in deep learning, and the execution of computation in the form of graphs in a tensorflow program.', 'duration': 515.355, 'highlights': ['The computational intensity of deep learning and the advantage of using GPUs for iterative calculations Deep learning applications are compute-intensive, especially during the training process, with large data sizes and numerous iterative processes, making GPUs advantageous for handling iterative calculations.', 'The role of TensorFlow as an open-source library for deep learning and its support for traditional machine learning TensorFlow, an open-source library developed by Google, primarily supports deep learning and also traditional machine learning, although it may have some overhead for traditional machine learning.', 'The use of tensors for data storage and handling in deep learning Tensors in TensorFlow provide a compact way of storing and handling data during computation, especially beneficial for deep learning training processes with large data.', 'The execution of computation in the form of graphs in a TensorFlow program In a TensorFlow program, computation occurs in the form of graphs, which are prepared and then executed in a session, representing a data flow graph and enabling execution on CPUs, GPUs, or in a distributed manner.']}], 'duration': 682.748, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs287342.jpg', 'highlights': ['TensorFlow simplifies the coding process by providing high-level APIs, reducing the complexity of writing code for tasks like configuring a neural network or programming a neuron.', 'The computational intensity of deep learning and the advantage of using GPUs for iterative calculations.', 'The key components for developing deep learning applications involve using programming languages such as Python, and leveraging popular libraries like TensorFlow, Keras, and Theano to facilitate the development process.', 'Tensors in TensorFlow provide a compact way of storing and handling data during computation, especially beneficial for deep learning training processes with large data.', 'In a TensorFlow program, computation occurs in the form of graphs, which are prepared and then executed in a session, representing a data flow graph and enabling execution on CPUs, GPUs, or in a distributed manner.']}, {'end': 1457.076, 'segs': [{'end': 996.32, 'src': 'embed', 'start': 970.09, 'weight': 1, 'content': [{'end': 974.835, 'text': 'a variable is a variable in your program, So you have anything that can keep changing.', 'start': 970.09, 'duration': 4.745}, {'end': 980.222, 'text': 'you just create as a variable, or even constants in fact are actually created as variables.', 'start': 974.835, 'duration': 5.387}, {'end': 984.907, 'text': 'But in TensorFlow, the storage in the program consists of three types.', 'start': 980.382, 'duration': 4.525}, {'end': 987.551, 'text': 'One is constants, another is variable.', 'start': 985.168, 'duration': 2.383}, {'end': 989.413, 'text': 'and the third is a placeholder.', 'start': 987.871, 'duration': 1.542}, {'end': 990.594, 'text': 'so, and they are.', 'start': 989.413, 'duration': 1.181}, {'end': 996.32, 'text': 'there is a lot of difference between these types and we will see how they vary and how they are used, and so on.', 'start': 990.594, 'duration': 5.726}], 'summary': 'In tensorflow, program storage consists of 3 types: constants, variables, and placeholders, each with distinct functions.', 'duration': 26.23, 'max_score': 970.09, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs970090.jpg'}, {'end': 1036.895, 'src': 'embed', 'start': 1010.395, 'weight': 0, 'content': [{'end': 1014.258, 'text': 'here, A slightly more advanced version, is you also specify the type?', 'start': 1010.395, 'duration': 3.863}, {'end': 1017.72, 'text': 'So you say tf.constant 2.0 tf.load 32.', 'start': 1014.318, 'duration': 3.402}, {'end': 1020.262, 'text': 'So the type is of type float.', 'start': 1017.72, 'duration': 2.542}, {'end': 1025.946, 'text': 'Now in case of constants you cannot during the computation you cannot really change these values.', 'start': 1020.363, 'duration': 5.583}, {'end': 1031.79, 'text': 'So for example if you want to change the value of b from 3 to 5 or any other number it is not possible.', 'start': 1025.987, 'duration': 5.803}, {'end': 1033.893, 'text': 'So that is the meaning of constant.', 'start': 1031.871, 'duration': 2.022}, {'end': 1034.613, 'text': 'all right.', 'start': 1034.173, 'duration': 0.44}, {'end': 1036.895, 'text': 'so then we have variables.', 'start': 1034.613, 'duration': 2.282}], 'summary': 'Tensorflow allows specifying type, constants are unchangeable during computation, then there are variables.', 'duration': 26.5, 'max_score': 1010.395, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1010395.jpg'}, {'end': 1180.69, 'src': 'embed', 'start': 1152.277, 'weight': 2, 'content': [{'end': 1155.439, 'text': 'Then you have variables which are like normal variables we are all familiar with.', 'start': 1152.277, 'duration': 3.162}, {'end': 1157.099, 'text': 'And then you have placeholders.', 'start': 1155.759, 'duration': 1.34}, {'end': 1160.361, 'text': 'This is primarily for feeding data from usually from outside.', 'start': 1157.16, 'duration': 3.201}, {'end': 1165.323, 'text': 'But of course you can also for temporary testing purpose you can feed within the program as well.', 'start': 1160.401, 'duration': 4.922}, {'end': 1168.965, 'text': 'But primarily the purpose of a placeholder is to get data from outside.', 'start': 1165.543, 'duration': 3.422}, {'end': 1174.007, 'text': 'So alright, so those were the constants, variables and placeholders.', 'start': 1169.125, 'duration': 4.882}, {'end': 1177.969, 'text': 'That is how you handle data within the TensorFlow program.', 'start': 1174.087, 'duration': 3.882}, {'end': 1180.69, 'text': 'And then you create a graph.', 'start': 1178.329, 'duration': 2.361}], 'summary': 'Tensorflow uses constants, variables, and placeholders to handle data and create a graph.', 'duration': 28.413, 'max_score': 1152.277, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1152277.jpg'}, {'end': 1219.435, 'src': 'embed', 'start': 1194.374, 'weight': 3, 'content': [{'end': 1204.203, 'text': 'and so typically what you need to do is every variable or a computation that you perform is like an operation or a node within a graph.', 'start': 1194.374, 'duration': 9.829}, {'end': 1208.307, 'text': 'so initially the graph will be what is known as the default graph.', 'start': 1204.203, 'duration': 4.104}, {'end': 1216.893, 'text': 'the moment you create a tensorflow object or TF, this TF here you see this is the TensorFlow object and again in the code, when we go into the code,', 'start': 1208.307, 'duration': 8.586}, {'end': 1219.435, 'text': 'it will become much easier to understand.', 'start': 1216.893, 'duration': 2.542}], 'summary': 'Tensorflow uses a graph structure with nodes representing variables and computations.', 'duration': 25.061, 'max_score': 1194.374, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1194374.jpg'}, {'end': 1337.373, 'src': 'embed', 'start': 1306.332, 'weight': 4, 'content': [{'end': 1311.653, 'text': 'So that is a different way of programming compared to our traditional way of writing programs.', 'start': 1306.332, 'duration': 5.321}, {'end': 1314.394, 'text': 'So you need to get used to this new format.', 'start': 1311.693, 'duration': 2.701}, {'end': 1321.721, 'text': 'And when we look at the code as we move forward and when we look in the Jupyter notebook, it will become much easier probably,', 'start': 1315.014, 'duration': 6.707}, {'end': 1323.883, 'text': 'to understand this rather than the slide.', 'start': 1321.721, 'duration': 2.162}, {'end': 1326.425, 'text': 'So these are the slides showing the code.', 'start': 1323.943, 'duration': 2.482}, {'end': 1337.373, 'text': 'But what we can do is go straight into the lab and take a look at the various examples that are there, and, starting from the very basic one,', 'start': 1326.866, 'duration': 10.507}], 'summary': 'Transition to new programming format in jupyter notebook for easier understanding.', 'duration': 31.041, 'max_score': 1306.332, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1306332.jpg'}, {'end': 1400.738, 'src': 'embed', 'start': 1372.956, 'weight': 5, 'content': [{'end': 1379.522, 'text': 'And we will in the end, we will take up a use case implementation using TensorFlow.', 'start': 1372.956, 'duration': 6.566}, {'end': 1388.088, 'text': "So let's first go and see those examples and then come back and I'll explain this use case and then we will execute the use case in the lab.", 'start': 1379.662, 'duration': 8.426}, {'end': 1391.131, 'text': "So let's go and check our lab.", 'start': 1388.188, 'duration': 2.943}, {'end': 1394.933, 'text': "Okay, so I'm in the Jupyter Notebook environment.", 'start': 1391.631, 'duration': 3.302}, {'end': 1399.517, 'text': 'And this is one of the development environments we can use.', 'start': 1395.274, 'duration': 4.243}, {'end': 1400.738, 'text': 'This is regular Python.', 'start': 1399.617, 'duration': 1.121}], 'summary': 'The use case will be implemented using tensorflow in a jupyter notebook environment.', 'duration': 27.782, 'max_score': 1372.956, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1372956.jpg'}], 'start': 970.09, 'title': 'Tensorflow basics', 'summary': 'Covers tensorflow constants, variables, and placeholders, programming basics, data handling, creating a graph, session objects, and practical use case implementation.', 'chapters': [{'end': 1135.785, 'start': 970.09, 'title': 'Tensorflow: constants, variables, and placeholders', 'summary': 'Explains the differences between constants, variables, and placeholders in tensorflow, emphasizing that constants cannot be changed, variables can be updated, and placeholders are used for feeding data from external sources.', 'duration': 165.695, 'highlights': ['Constants in TensorFlow cannot be changed during computation and are defined using tf.constant. Constants in TensorFlow, once defined, cannot be changed during computation, and are defined using tf.constant.', 'Variables in TensorFlow can be updated with different values and are defined using tf.variable. Variables in TensorFlow can be updated with different values and are defined using tf.variable.', 'Placeholders in TensorFlow are used for feeding data from external sources and are particularly useful for handling memory when dealing with large datasets. Placeholders in TensorFlow are used for feeding data from external sources and are particularly useful for handling memory when dealing with large datasets.']}, {'end': 1457.076, 'start': 1135.905, 'title': 'Tensorflow programming basics', 'summary': 'Covers the basics of tensorflow programming, including the types of data handling (constants, variables, placeholders), creating a graph, session objects, and executing computations within tensorflow. it also emphasizes the transition to a different programming style and the practical use case implementation using tensorflow.', 'duration': 321.171, 'highlights': ['TensorFlow programming involves handling three types of data: constants, variables, and placeholders, primarily for feeding data from outside, and each of them is considered an operation. TensorFlow programming involves handling three types of data: constants, variables, and placeholders, primarily for feeding data from outside, and each of them is considered an operation.', 'Creating a graph and session objects in TensorFlow precede the execution of computations, with each variable or computation being considered an operation or a node within the graph. Creating a graph and session objects in TensorFlow precede the execution of computations, with each variable or computation being considered an operation or a node within the graph.', 'The chapter emphasizes the transition to a different programming style in TensorFlow, requiring familiarity with its terminology and operation-based approach. The chapter emphasizes the transition to a different programming style in TensorFlow, requiring familiarity with its terminology and operation-based approach.', 'The practical use case implementation using TensorFlow is also mentioned, requiring some basic machine learning concepts for understanding. The practical use case implementation using TensorFlow is also mentioned, requiring some basic machine learning concepts for understanding.']}], 'duration': 486.986, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs970090.jpg', 'highlights': ['Constants in TensorFlow cannot be changed during computation and are defined using tf.constant.', 'Variables in TensorFlow can be updated with different values and are defined using tf.variable.', 'Placeholders in TensorFlow are used for feeding data from external sources and are particularly useful for handling memory when dealing with large datasets.', 'Creating a graph and session objects in TensorFlow precede the execution of computations, with each variable or computation being considered an operation or a node within the graph.', 'The chapter emphasizes the transition to a different programming style in TensorFlow, requiring familiarity with its terminology and operation-based approach.', 'The practical use case implementation using TensorFlow is also mentioned, requiring some basic machine learning concepts for understanding.']}, {'end': 2074.478, 'segs': [{'end': 1508.711, 'src': 'embed', 'start': 1477.58, 'weight': 2, 'content': [{'end': 1481.521, 'text': 'Okay So this will import the TensorFlow into my session.', 'start': 1477.58, 'duration': 3.941}, {'end': 1484.762, 'text': 'Now, this is the way to create a variable.', 'start': 1481.761, 'duration': 3.001}, {'end': 1489.644, 'text': "So let's start by creating a variable and this is the name of my variable.", 'start': 1484.902, 'duration': 4.742}, {'end': 1498.767, 'text': "I'm starting by giving a name called zero and I say zero is equal to tf dot variable and then I'm giving the value of the variable here.", 'start': 1489.784, 'duration': 8.983}, {'end': 1503.329, 'text': 'So this is the very basic way and the simplest way to create a variable.', 'start': 1498.847, 'duration': 4.482}, {'end': 1508.711, 'text': 'We will see a little later there are other formats as well, but the bare minimum way of creating a variable.', 'start': 1503.509, 'duration': 5.202}], 'summary': "Import tensorflow, create variable named 'zero' with tf.variable, basic way of creating variable.", 'duration': 31.131, 'max_score': 1477.58, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1477580.jpg'}, {'end': 1752.448, 'src': 'embed', 'start': 1722.466, 'weight': 1, 'content': [{'end': 1724.327, 'text': "I'll come back to this in a bit.", 'start': 1722.466, 'duration': 1.861}, {'end': 1730.312, 'text': "But let's say we start by creating you remember, I told you we need to create a session.", 'start': 1724.928, 'duration': 5.384}, {'end': 1734.736, 'text': 'So the way to create a session, there are a couple of ways of creating a session.', 'start': 1730.632, 'duration': 4.104}, {'end': 1737.738, 'text': 'But this is for beginners, this is the easiest way.', 'start': 1734.936, 'duration': 2.802}, {'end': 1742.482, 'text': 'All you need to do is assign a variable called says or you can give any name.', 'start': 1738.058, 'duration': 4.424}, {'end': 1745.224, 'text': 'And that is equal to TF dot session.', 'start': 1742.862, 'duration': 2.362}, {'end': 1746.844, 'text': 'That is a session method here.', 'start': 1745.524, 'duration': 1.32}, {'end': 1749.746, 'text': 'And you create a session object by the name says.', 'start': 1747.185, 'duration': 2.561}, {'end': 1752.448, 'text': "Now what I'll do is I'll skip this as well.", 'start': 1750.026, 'duration': 2.422}], 'summary': 'Creating a session using tf dot session is the easiest way for beginners.', 'duration': 29.982, 'max_score': 1722.466, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1722466.jpg'}, {'end': 1947.092, 'src': 'embed', 'start': 1922.477, 'weight': 0, 'content': [{'end': 1929.862, 'text': 'And we have also seen if you have variables that you need to execute or have these two lines of code to initialize the variables.', 'start': 1922.477, 'duration': 7.385}, {'end': 1934.405, 'text': 'And then we have seen that after creating a graph, how to run the graph in a session.', 'start': 1930.183, 'duration': 4.222}, {'end': 1941.189, 'text': 'And I will show you a little bit more in detail how exactly the graph gets created and how it gets executed.', 'start': 1934.605, 'duration': 6.584}, {'end': 1947.092, 'text': 'But this was the first very quick code on creating variables and constants.', 'start': 1941.489, 'duration': 5.603}], 'summary': 'Demonstrated initialization and execution of variables and graph in a session.', 'duration': 24.615, 'max_score': 1922.477, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1922477.jpg'}], 'start': 1457.076, 'title': 'Tensorflow basics and code basics', 'summary': 'Covers creating variables, constants, and executing operations in tensorflow. it also explains the process of building a graph and initializing variables in a session, with practical examples included.', 'chapters': [{'end': 1722.406, 'start': 1457.076, 'title': 'Tensorflow basics: creating variables and constants', 'summary': 'Explains the process of importing tensorflow, creating variables and constants, and the restrictions on modifying variables and constants in tensorflow, including the method of creating them and the limitations on modifying them. it also mentions the concept of building a graph without executing any tensorflow code.', 'duration': 265.33, 'highlights': ["The process of importing TensorFlow and creating variables and constants is explained. The chapter discusses the process of importing TensorFlow and creating variables named 'zero' and 'one' as well as constants using tf.variable and tf.constant methods.", 'The restrictions on modifying variables and constants in TensorFlow are outlined. The chapter explains the limitations on modifying variables and constants, highlighting that only variables can be modified, while constants cannot be modified in TensorFlow.', 'The concept of building a graph without executing any TensorFlow code is introduced. It is mentioned that the process involves building a graph without executing any TensorFlow code, indicating that the focus is on creating the graph structure without actual execution.']}, {'end': 2074.478, 'start': 1722.466, 'title': 'Tensorflow code basics', 'summary': 'Covers the basics of creating and running a tensorflow graph, including initializing variables and executing operations in a session, with examples of creating constants, variables, and strings, and running a for loop.', 'duration': 352.012, 'highlights': ['Creating a session object by assigning a variable to TF dot session is the easiest way for beginners. This method is recommended for beginners and provides the simplest way to create a session object.', 'The need to initialize variables and execute specific operations before running the graph in a session to avoid errors. It is essential to initialize variables and execute specific operations, such as tf.global_variables_initializer and session.run, before running the graph in a session to prevent errors.', 'Demonstrating the creation of constants, variables, and strings, and running a for loop to execute specific operations in a session. The chapter includes examples of creating constants, variables, and strings, and running a for loop to execute specific operations within a session.']}], 'duration': 617.402, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs1457076.jpg', 'highlights': ['The need to initialize variables and execute specific operations before running the graph in a session to avoid errors. It is essential to initialize variables and execute specific operations, such as tf.global_variables_initializer and session.run, before running the graph in a session to prevent errors.', 'Creating a session object by assigning a variable to TF dot session is the easiest way for beginners. This method is recommended for beginners and provides the simplest way to create a session object.', "The process of importing TensorFlow and creating variables and constants is explained. The chapter discusses the process of importing TensorFlow and creating variables named 'zero' and 'one' as well as constants using tf.variable and tf.constant methods."]}, {'end': 2502.379, 'segs': [{'end': 2122.975, 'src': 'embed', 'start': 2094.799, 'weight': 0, 'content': [{'end': 2100.683, 'text': 'So this is slightly more complicated and something new even for people who have been writing programs.', 'start': 2094.799, 'duration': 5.884}, {'end': 2104.005, 'text': "So, but I'll just explain it with a quick example.", 'start': 2100.743, 'duration': 3.262}, {'end': 2111.79, 'text': 'First of all, how do you declare or create a placeholder? You just do tf.placeholder and the P is lowercase.', 'start': 2104.265, 'duration': 7.525}, {'end': 2117.314, 'text': 'Only in case of variables, the V is uppercase, but otherwise constant and placeholders it is lowercase.', 'start': 2112.131, 'duration': 5.183}, {'end': 2122.975, 'text': "And you just say, because as I mentioned, this is a placeholder that doesn't have any value.", 'start': 2117.774, 'duration': 5.201}], 'summary': 'Introducing a new concept in programming: tf.placeholder for creating placeholders without values.', 'duration': 28.176, 'max_score': 2094.799, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2094799.jpg'}], 'start': 2074.518, 'title': 'Tensorflow placeholders and computation', 'summary': 'Delves into the concept of placeholders in tensorflow, encompassing declaration, value population, and diverse computations. it illustrates their usage with scalar and multi-dimensional arrays.', 'chapters': [{'end': 2502.379, 'start': 2074.518, 'title': 'Tensorflow: placeholders and computation', 'summary': 'Covers the concept of placeholders in tensorflow, including the declaration, populating with values, and examples of computations with different types of placeholders, demonstrating their usage with scalar and multi-dimensional arrays.', 'duration': 427.861, 'highlights': ['Placeholder declaration: tf.placeholder is used to create a placeholder with a specified type, such as tf.float32 for floating point values.', 'Populating placeholders: Placeholders are populated using dictionaries in TensorFlow, with examples of feeding scalar and multi-dimensional arrays.', 'Computation with placeholders: Demonstrates the concept of placeholders in action by performing computations using the populated placeholder values, showcasing the flexibility and usage of placeholders in TensorFlow.']}], 'duration': 427.861, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2074518.jpg', 'highlights': ['Demonstrates the concept of placeholders in action by performing computations using the populated placeholder values, showcasing the flexibility and usage of placeholders in TensorFlow.', 'Placeholders are populated using dictionaries in TensorFlow, with examples of feeding scalar and multi-dimensional arrays.', 'Placeholder declaration: tf.placeholder is used to create a placeholder with a specified type, such as tf.float32 for floating point values.']}, {'end': 3167.981, 'segs': [{'end': 2531.418, 'src': 'embed', 'start': 2502.499, 'weight': 2, 'content': [{'end': 2508.546, 'text': 'So that is the idea behind having these placeholders and the way we feed these placeholders.', 'start': 2502.499, 'duration': 6.047}, {'end': 2509.507, 'text': 'there is a provision.', 'start': 2508.546, 'duration': 0.961}, {'end': 2516.875, 'text': "there's a deliberately, there has been made a provision for this kind of getting data into the program in chunks.", 'start': 2509.507, 'duration': 7.368}, {'end': 2521.736, 'text': 'Okay, now, in this particular example, we will close the session here.', 'start': 2517.415, 'duration': 4.321}, {'end': 2525.757, 'text': 'And then I will show you what is the other way of creating a session right?', 'start': 2522.016, 'duration': 3.741}, {'end': 2531.418, 'text': 'So now, if I close the session here now, if I try to do anything with the session, it will give us an error.', 'start': 2525.977, 'duration': 5.441}], 'summary': 'Placeholder feeding and provision for chunk data input in program demonstrated, with a session closing example.', 'duration': 28.919, 'max_score': 2502.499, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2502499.jpg'}, {'end': 2598.478, 'src': 'embed', 'start': 2568.864, 'weight': 0, 'content': [{'end': 2573.57, 'text': "so what will happen is you don't have to explicitly close the session.", 'start': 2568.864, 'duration': 4.706}, {'end': 2578.776, 'text': 'the moment this with block gets completed, the session gets closed.', 'start': 2573.57, 'duration': 5.206}, {'end': 2581.719, 'text': "okay, so let's just take a quick look at this example.", 'start': 2578.776, 'duration': 2.943}, {'end': 2586.885, 'text': 'now if i uh, let me clear this current output be clear.', 'start': 2581.719, 'duration': 5.166}, {'end': 2588.907, 'text': 'okay. so this is the hello world example.', 'start': 2586.885, 'duration': 2.022}, {'end': 2591.15, 'text': 'a little bit earlier we did that.', 'start': 2588.907, 'duration': 2.243}, {'end': 2593.372, 'text': "so i'm doing a with tf dot.", 'start': 2591.15, 'duration': 2.222}, {'end': 2598.478, 'text': 'session assessment result is equal to says dot, run hello plus world and then i say print result.', 'start': 2593.372, 'duration': 5.106}], 'summary': "Using 'with' block automatically closes session in tensorflow example.", 'duration': 29.614, 'max_score': 2568.864, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2568864.jpg'}, {'end': 2693.352, 'src': 'embed', 'start': 2663.463, 'weight': 1, 'content': [{'end': 2667.987, 'text': "Only thing is that it's a good practice and your resources will get released.", 'start': 2663.463, 'duration': 4.524}, {'end': 2673.773, 'text': 'otherwise, If you do multiple of these programs, if they are running, then your resources will get blocked.', 'start': 2667.987, 'duration': 5.786}, {'end': 2674.553, 'text': "That's the only thing.", 'start': 2673.813, 'duration': 0.74}, {'end': 2677.656, 'text': 'And to start with, it is simpler to do it this way.', 'start': 2674.754, 'duration': 2.902}, {'end': 2680.499, 'text': "So initially when you're doing, you start with this.", 'start': 2678.017, 'duration': 2.482}, {'end': 2688.347, 'text': 'But as you move forward, as you become familiar with TensorFlow programming, I would recommend all of you to get into this model.', 'start': 2680.84, 'duration': 7.507}, {'end': 2693.352, 'text': 'Most of the programming, TensorFlow programming is done like this with tf.session.ss.', 'start': 2688.807, 'duration': 4.545}], 'summary': 'Using tf.session.ss is recommended for tensorflow programming to avoid resource blocking.', 'duration': 29.889, 'max_score': 2663.463, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2663463.jpg'}, {'end': 2873.674, 'src': 'embed', 'start': 2844.36, 'weight': 3, 'content': [{'end': 2846.681, 'text': "this additional name doesn't have much value.", 'start': 2844.36, 'duration': 2.321}, {'end': 2851.322, 'text': "in this our case also, we will be seeing the graph, and that's why i'm using this,", 'start': 2846.681, 'duration': 4.641}, {'end': 2855.423, 'text': "but otherwise this having additionally a name usually doesn't add much value.", 'start': 2851.322, 'duration': 4.101}, {'end': 2857.464, 'text': "Alright. so let's create a constant.", 'start': 2855.723, 'duration': 1.741}, {'end': 2859.105, 'text': 'I created a constant.', 'start': 2858.104, 'duration': 1.001}, {'end': 2861.907, 'text': "Now let us see what's going on in the graph.", 'start': 2859.425, 'duration': 2.482}, {'end': 2866.309, 'text': 'So I will just say operations is equal to get operations.', 'start': 2862.367, 'duration': 3.942}, {'end': 2870.452, 'text': 'And if you see here now, you remember here it was blank.', 'start': 2866.91, 'duration': 3.542}, {'end': 2873.674, 'text': 'Now, you see, the first operation is showing up and this a.', 'start': 2870.912, 'duration': 2.762}], 'summary': 'Creating a constant and observing graph operations, seeing the first operation showing up.', 'duration': 29.314, 'max_score': 2844.36, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2844360.jpg'}, {'end': 3063.655, 'src': 'embed', 'start': 3032.073, 'weight': 4, 'content': [{'end': 3043.502, 'text': 'So now you have built a Fairly simple, but operation graph consists of A, B, C, D, E, 1, 2, 3, 4, 5 operations.', 'start': 3032.073, 'duration': 11.429}, {'end': 3047.885, 'text': "Okay, so you built a small, it's not a very complicated one, but a simple graph.", 'start': 3043.822, 'duration': 4.063}, {'end': 3051.548, 'text': 'Now you need to execute these operations.', 'start': 3048.285, 'duration': 3.263}, {'end': 3054.03, 'text': 'So what you need to do, you need to create a session.', 'start': 3051.728, 'duration': 2.302}, {'end': 3063.655, 'text': 'So, ses.tf is equal to session and then you can run these session print ses.runE.', 'start': 3054.81, 'duration': 8.845}], 'summary': 'Built simple graph with a, b, c, d, e, 1, 2, 3, 4, 5 operations and executed with session', 'duration': 31.582, 'max_score': 3032.073, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3032073.jpg'}], 'start': 2502.499, 'title': 'Tensorflow sessions and graph building', 'summary': 'Covers tensorflow session management, including creating and managing sessions, emphasizing the importance of resource release. it also explains building a tensorflow graph, creating constants, variables, placeholders, and executing operations for computations.', 'chapters': [{'end': 2680.499, 'start': 2502.499, 'title': 'Tensorflow session management', 'summary': 'Discusses different ways of creating and managing sessions in tensorflow, including using placeholders, with blocks, session closing, and resource management, emphasizing the importance of closing sessions to release resources.', 'duration': 178, 'highlights': ["Using a 'with' block to create a session allows for automatic session closure, simplifying session management.", 'The importance of explicitly closing sessions to release resources and avoid resource blocking is emphasized.', 'The chapter introduces the concept of placeholders and explains the provision for feeding data into the program in chunks.']}, {'end': 3167.981, 'start': 2680.84, 'title': 'Tensorflow graph building', 'summary': 'Explains how to build a tensorflow graph, create constants, variables, and placeholders, and execute operations to perform computations within the graph.', 'duration': 487.141, 'highlights': ['The chapter explains how to create constants, give them names, and observe the operations performed on the graph. The transcript explains the process of creating constants, giving them names, and observing the operations performed on the graph, demonstrating the method of building a TensorFlow graph.', 'It details the creation of a graph with operations involving constants, addition, and multiplication. The transcript illustrates the creation of a graph with operations involving constants, addition, and multiplication, providing a practical demonstration of graph building in TensorFlow.', 'It emphasizes the process of executing operations within the graph by creating a session and running specific operations. The chapter emphasizes the process of executing operations within the graph by creating a session and running specific operations, highlighting the practical application of graph execution in TensorFlow.']}], 'duration': 665.482, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs2502499.jpg', 'highlights': ["Using a 'with' block to create a session allows for automatic session closure, simplifying session management.", 'The importance of explicitly closing sessions to release resources and avoid resource blocking is emphasized.', 'The chapter introduces the concept of placeholders and explains the provision for feeding data into the program in chunks.', 'It details the creation of a graph with operations involving constants, addition, and multiplication.', 'It emphasizes the process of executing operations within the graph by creating a session and running specific operations.']}, {'end': 3788.039, 'segs': [{'end': 3187.092, 'src': 'embed', 'start': 3168.841, 'weight': 0, 'content': [{'end': 3181.889, 'text': 'I hope it was helpful in understanding how variables and constants and placeholders are created in the previous example we saw and how exactly you create a graph and then you actually execute that graph.', 'start': 3168.841, 'duration': 13.048}, {'end': 3184.71, 'text': "That's the way TensorFlow program works.", 'start': 3182.149, 'duration': 2.561}, {'end': 3187.092, 'text': "That's the structure of TensorFlow program.", 'start': 3184.77, 'duration': 2.322}], 'summary': 'Understanding creation and execution of variables, constants, and placeholders in tensorflow.', 'duration': 18.251, 'max_score': 3168.841, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3168841.jpg'}, {'end': 3285.639, 'src': 'embed', 'start': 3241.21, 'weight': 1, 'content': [{'end': 3251.292, 'text': 'we have to build a model for classifying whether the income of this person is above 50k or below 50k.', 'start': 3241.21, 'duration': 10.082}, {'end': 3254.353, 'text': 'okay, so there is actually a label data available.', 'start': 3251.292, 'duration': 3.061}, {'end': 3263.374, 'text': 'we will then try to build a model and then see what is the accuracy, so that this is a typical machine learning problem actually, but as i said,', 'start': 3254.353, 'duration': 9.021}, {'end': 3265.815, 'text': 'TensorFlow can be used for doing machine learning as well.', 'start': 3263.374, 'duration': 2.441}, {'end': 3266.916, 'text': 'So we will.', 'start': 3266.155, 'duration': 0.761}, {'end': 3269.519, 'text': 'as a simple example, we will take machine learning.', 'start': 3266.916, 'duration': 2.603}, {'end': 3272.363, 'text': 'So this is how the high level the code looks.', 'start': 3269.72, 'duration': 2.643}, {'end': 3280.072, 'text': 'And in TensorFlow programming we also take help of regular Python libraries like, for example,', 'start': 3272.643, 'duration': 7.429}, {'end': 3285.639, 'text': 'it could be NumPy or It could be scikit learn or it could be pandas in this case.', 'start': 3280.072, 'duration': 5.567}], 'summary': 'Building a model to classify income using tensorflow and achieving accuracy through machine learning.', 'duration': 44.429, 'max_score': 3241.21, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3241210.jpg'}, {'end': 3406.953, 'src': 'embed', 'start': 3372.119, 'weight': 3, 'content': [{'end': 3374.162, 'text': 'So, here we are importing the data.', 'start': 3372.119, 'duration': 2.043}, {'end': 3375.023, 'text': 'the data is.', 'start': 3374.162, 'duration': 0.861}, {'end': 3378.186, 'text': 'the name of the file is census underscore data dot CSV.', 'start': 3375.023, 'duration': 3.163}, {'end': 3379.248, 'text': "it's a CSV file.", 'start': 3378.186, 'duration': 1.062}, {'end': 3385.035, 'text': 'And then there is income bracket is one of the columns, which basically is our target.', 'start': 3379.468, 'duration': 5.567}, {'end': 3389.56, 'text': "That is what is used that is the label rather, but this doesn't have numeric value.", 'start': 3385.255, 'duration': 4.305}, {'end': 3391.442, 'text': 'So it has the income.', 'start': 3389.64, 'duration': 1.802}, {'end': 3397.387, 'text': 'we need to convert that into binary values, either zero or one.', 'start': 3392.363, 'duration': 5.024}, {'end': 3398.968, 'text': "So that's what we are doing here.", 'start': 3397.707, 'duration': 1.261}, {'end': 3406.953, 'text': 'And then we are splitting using scikit learn, we are doing splitting of the data into test and training, right.', 'start': 3399.368, 'duration': 7.585}], 'summary': 'Importing census data from csv file, converting income bracket to binary values, and splitting data into test and training sets using scikit-learn.', 'duration': 34.834, 'max_score': 3372.119, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3372119.jpg'}, {'end': 3697.759, 'src': 'embed', 'start': 3669.602, 'weight': 4, 'content': [{'end': 3672.524, 'text': 'So, once I run this, what is, what are we doing here?', 'start': 3669.602, 'duration': 2.922}, {'end': 3681.629, 'text': "Again, those are familiar with scikit learn will immediately recognize I'm splitting this into test and train and saying test size is 30%.", 'start': 3672.984, 'duration': 8.645}, {'end': 3685.231, 'text': 'So 30% of the data should go into test and training should have 70%.', 'start': 3681.629, 'duration': 3.602}, {'end': 3689.734, 'text': "Okay, so that's all we are doing point three indicates 30%.", 'start': 3685.231, 'duration': 4.503}, {'end': 3693.356, 'text': 'So you can again, this can be individual preferences.', 'start': 3689.734, 'duration': 3.622}, {'end': 3697.759, 'text': 'Some people do it like 5050, some people 2080.', 'start': 3693.476, 'duration': 4.283}], 'summary': 'Splitting data into 70% training and 30% testing.', 'duration': 28.157, 'max_score': 3669.602, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3669602.jpg'}], 'start': 3168.841, 'title': 'Tensorflow programming and model training', 'summary': 'Explains tensorflow programming structure using high-level apis to create a linear classifier for a machine learning problem with real census data and covers importing, preprocessing, and training a classification model with specific steps and tools.', 'chapters': [{'end': 3372.019, 'start': 3168.841, 'title': 'Tensorflow programming structure', 'summary': "Explains the structure of tensorflow programming, using high-level apis like estimator api to create a linear classifier for a machine learning problem with real census data, aiming to classify whether a person's income is above or below 50k.", 'duration': 203.178, 'highlights': ['The chapter explains the structure of TensorFlow programming, including the creation of variables, constants, and placeholders, as well as the execution of a graph. TensorFlow is used for machine learning, and the high-level APIs like estimator API are employed to create a linear classifier for a classification problem with real census data. (Relevance: 5)', "The example involves a classification problem using real census data, where the goal is to classify whether a person's income is above 50k or below 50k, utilizing the label data available and aiming to measure the accuracy of the model. (Relevance: 4)", 'Regular Python libraries such as NumPy, scikit-learn, and pandas are utilized in TensorFlow programming to prepare and format the data before performing machine learning or deep learning activities. (Relevance: 3)', 'TensorFlow offers high-level APIs, such as the estimator API, which is used to create a linear classifier. The data needs to be prepared and structured in a certain way before utilizing the API. (Relevance: 2)', 'Before training the model, the data needs to be prepared into a certain format, including the splitting of data into training and test datasets, which can be done using regular Python libraries like pandas and scikit-learn. (Relevance: 1)']}, {'end': 3788.039, 'start': 3372.119, 'title': 'Tensorflow data preprocessing and model training', 'summary': 'Covers importing and preprocessing data from a csv file, converting income bracket to binary values, splitting the data into test and training sets using scikit-learn, creating feature columns and input functions for the tensorflow estimator api, and training and evaluating a classification model.', 'duration': 415.92, 'highlights': ['The data is imported from a CSV file named census_data.csv and the income bracket column is targeted for conversion into binary values. The chapter starts with importing data from a CSV file named census_data.csv and focuses on converting the income bracket column into binary values.', "The data is split into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing. The chapter demonstrates splitting the data into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing.", 'The process involves creating feature columns and input functions for the TensorFlow estimator API, followed by training and evaluating the classification model. The process involves creating feature columns and input functions for the TensorFlow estimator API, followed by training and evaluating the classification model.']}], 'duration': 619.198, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3168841.jpg', 'highlights': ['The chapter explains the structure of TensorFlow programming, including the creation of variables, constants, and placeholders, as well as the execution of a graph. TensorFlow is used for machine learning, and the high-level APIs like estimator API are employed to create a linear classifier for a classification problem with real census data.', "The example involves a classification problem using real census data, where the goal is to classify whether a person's income is above 50k or below 50k, utilizing the label data available and aiming to measure the accuracy of the model.", 'Regular Python libraries such as NumPy, scikit-learn, and pandas are utilized in TensorFlow programming to prepare and format the data before performing machine learning or deep learning activities.', 'The data is imported from a CSV file named census_data.csv and the income bracket column is targeted for conversion into binary values. The chapter starts with importing data from a CSV file named census_data.csv and focuses on converting the income bracket column into binary values.', "The data is split into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing. The chapter demonstrates splitting the data into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing."]}, {'end': 4462.541, 'segs': [{'end': 3834.908, 'src': 'embed', 'start': 3809.867, 'weight': 1, 'content': [{'end': 3819.895, 'text': 'Okay, so for categorical values, you need to use what is known as tf dot feature column dot categorical column with vocabulary list.', 'start': 3809.867, 'duration': 10.028}, {'end': 3824.98, 'text': 'And again, within this again, there are two ways in which you can create for categorical values.', 'start': 3820.136, 'duration': 4.844}, {'end': 3830.925, 'text': 'One is a vocabulary list where you know how many types are there.', 'start': 3825.46, 'duration': 5.465}, {'end': 3834.908, 'text': 'So for example, gender column can have only male and female.', 'start': 3830.945, 'duration': 3.963}], 'summary': 'Use tf.feature_column.categorical_column_with_vocabulary_list for categorical values, such as gender with male and female.', 'duration': 25.041, 'max_score': 3809.867, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3809867.jpg'}, {'end': 4117.256, 'src': 'embed', 'start': 4091.497, 'weight': 2, 'content': [{'end': 4098.545, 'text': 'So if you have 1000 records and you pass all the 1000 records three times for training purpose,', 'start': 4091.497, 'duration': 7.048}, {'end': 4104.488, 'text': 'then you call that as three epochs, right? So there is a difference between the batch size and epochs.', 'start': 4099.024, 'duration': 5.464}, {'end': 4110.032, 'text': "So when let's say we have 1000 records, and you're saying batch size is 100.", 'start': 4104.849, 'duration': 5.183}, {'end': 4117.256, 'text': 'That means there will be 10 batches, right? So if you take 10 batches, then That is one epoch gets completed.', 'start': 4110.032, 'duration': 7.224}], 'summary': 'Training 1000 records for 3 epochs with batch size of 100 creates 10 batches per epoch.', 'duration': 25.759, 'max_score': 4091.497, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs4091497.jpg'}, {'end': 4218.026, 'src': 'embed', 'start': 4187.015, 'weight': 4, 'content': [{'end': 4190.435, 'text': 'So model dot train, and this needs the input function.', 'start': 4187.015, 'duration': 3.42}, {'end': 4192.457, 'text': 'Remember, we created the input function.', 'start': 4190.475, 'duration': 1.982}, {'end': 4194.176, 'text': 'So we need to pass the input function.', 'start': 4192.517, 'duration': 1.659}, {'end': 4201.358, 'text': 'And then we say this steps is now telling how many iterations this training has to be run.', 'start': 4194.557, 'duration': 6.801}, {'end': 4202.879, 'text': 'So we are saying 5000.', 'start': 4201.678, 'duration': 1.201}, {'end': 4206.999, 'text': 'And this may be it will probably take a little long.', 'start': 4202.879, 'duration': 4.12}, {'end': 4209.04, 'text': "So we will okay, but that's fine.", 'start': 4207.039, 'duration': 2.001}, {'end': 4210.7, 'text': 'I think we will leave it 5000.', 'start': 4209.1, 'duration': 1.6}, {'end': 4218.026, 'text': 'Sometimes, if you have probably a less powerful machine, you can cut it down to maybe 1000 or something like that.', 'start': 4210.7, 'duration': 7.326}], 'summary': 'Train the model for 5000 iterations for optimal performance.', 'duration': 31.011, 'max_score': 4187.015, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs4187015.jpg'}, {'end': 4382.412, 'src': 'embed', 'start': 4352.943, 'weight': 0, 'content': [{'end': 4358.008, 'text': "This is pretty much like you're getting like almost 85% accuracy, which is pretty okay.", 'start': 4352.943, 'duration': 5.065}, {'end': 4361.769, 'text': 'And there are other ways to increase the accuracy.', 'start': 4358.608, 'duration': 3.161}, {'end': 4365.429, 'text': 'For example, you can run the training for more iterations.', 'start': 4361.809, 'duration': 3.62}, {'end': 4371.19, 'text': 'that is one way to get more data, and so on and so forth, or use, this is a linear classifier.', 'start': 4365.429, 'duration': 5.761}, {'end': 4373.751, 'text': 'we could have used a nonlinear classifier and so on.', 'start': 4371.19, 'duration': 2.561}, {'end': 4377.312, 'text': 'So again, multiple ways of doing it to increase the accuracy.', 'start': 4373.791, 'duration': 3.521}, {'end': 4382.412, 'text': 'But the idea here was to quickly show you a piece of TensorFlow code.', 'start': 4377.352, 'duration': 5.06}], 'summary': 'Using tensorflow code achieved 85% accuracy, can increase with more data and iterations', 'duration': 29.469, 'max_score': 4352.943, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs4352943.jpg'}, {'end': 4419.93, 'src': 'embed', 'start': 4395.423, 'weight': 5, 'content': [{'end': 4405.734, 'text': 'What is deep learning and what are the various libraries that are available for writing deep learning programs? And then we focused on TensorFlow.', 'start': 4395.423, 'duration': 10.311}, {'end': 4407.075, 'text': 'We saw what is TensorFlow?', 'start': 4405.774, 'duration': 1.301}, {'end': 4408.537, 'text': 'What are the elements of TensorFlow?', 'start': 4407.115, 'duration': 1.422}, {'end': 4411.164, 'text': 'how to write a TensorFlow program.', 'start': 4408.902, 'duration': 2.262}, {'end': 4413.565, 'text': 'what are the elements of a TensorFlow program?', 'start': 4411.164, 'duration': 2.401}, {'end': 4419.93, 'text': 'what are the various types storage types, like variables and placeholders, and constants, and what is the difference between them,', 'start': 4413.565, 'duration': 6.365}], 'summary': 'Introduction to tensorflow and its key elements for writing deep learning programs.', 'duration': 24.507, 'max_score': 4395.423, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs4395423.jpg'}], 'start': 3788.746, 'title': 'Feature columns and tensorflow classification', 'summary': 'Covers creating feature columns for categorical and continuous values using methods like tf.feature_column.categorical_column_with_vocabulary_list and tf.feature_column.numeric_column, and the use of input functions with batch size and epochs for training. it also discusses the evaluation of a machine learning model achieving 85% accuracy, ways to increase accuracy through training iterations and using different classifiers, and provides an overview of tensorflow and its elements.', 'chapters': [{'end': 4236.738, 'start': 3788.746, 'title': 'Creating feature columns for machine learning', 'summary': 'Covers the process of creating feature columns for categorical and continuous values, including methods such as tf.feature_column.categorical_column_with_vocabulary_list and tf.feature_column.numeric_column, and the use of input functions with batch size and epochs for training.', 'duration': 447.992, 'highlights': ['The process of creating feature columns for categorical and continuous values, including methods such as tf.feature_column.categorical_column_with_vocabulary_list and tf.feature_column.numeric_column.', 'The usage of input functions with batch size and epochs for training, where batch size determines the number of records read at a time and epochs indicate the number of times the entire data is passed through the model for training.', 'The creation of linear classifier using tf.estimator.LinearClassifier and the training of the model with a specified number of iterations, such as 5000 steps.']}, {'end': 4462.541, 'start': 4236.798, 'title': 'Tensorflow classification and evaluation', 'summary': 'Covers the evaluation of a machine learning model using scikit learn, achieving 85% accuracy and discusses ways to increase accuracy through training iterations and using different classifiers, while also providing an overview of tensorflow and its elements.', 'duration': 225.743, 'highlights': ['The model achieved an accuracy of almost 85% using scikit learn.', 'Discussed ways to increase accuracy through training iterations and using different classifiers.', 'Provided an overview of TensorFlow, its elements, and how to write a TensorFlow program.', 'Explained the various types of storage types in TensorFlow, such as variables, placeholders, and constants, and the difference between them.', 'Covered how to construct a graph and execute a graph in TensorFlow.']}], 'duration': 673.795, 'thumbnail': 'https://coursnap.oss-ap-southeast-1.aliyuncs.com/video-capture/E8n_k6HNAgs/pics/E8n_k6HNAgs3788746.jpg', 'highlights': ['The model achieved an accuracy of almost 85% using scikit learn.', 'The process of creating feature columns for categorical and continuous values, including methods such as tf.feature_column.categorical_column_with_vocabulary_list and tf.feature_column.numeric_column.', 'The usage of input functions with batch size and epochs for training, where batch size determines the number of records read at a time and epochs indicate the number of times the entire data is passed through the model for training.', 'Discussed ways to increase accuracy through training iterations and using different classifiers.', 'The creation of linear classifier using tf.estimator.LinearClassifier and the training of the model with a specified number of iterations, such as 5000 steps.', 'Provided an overview of TensorFlow, its elements, and how to write a TensorFlow program.']}], 'highlights': ['Covers basics of TensorFlow, its relation to deep learning, available libraries, advantages of using TensorFlow, and programming in Python.', 'Deep learning is a subset of machine learning primarily using neural networks, serving as the underlying technology behind artificial intelligence, teaching recognition of complex data such as images, audio, and text.', 'The tutorial emphasizes the importance of having some understanding of machine learning and Python before delving into the content, suggesting that viewers should refer to other tutorials if they are new to these concepts.', 'The session includes a basic introductory session for beginners and an implementation of TensorFlow code for a comprehensive understanding.', 'TensorFlow consists of tensors executed in a graphical format, and the programming language used will be Python.', 'The structure and function of neural networks, including the input layer, hidden layers, and output layer, are explained, with a focus on their roles in processing data and making predictions.', 'TensorFlow simplifies the coding process by providing high-level APIs, reducing the complexity of writing code for tasks like configuring a neural network or programming a neuron.', 'The computational intensity of deep learning and the advantage of using GPUs for iterative calculations.', 'The key components for developing deep learning applications involve using programming languages such as Python, and leveraging popular libraries like TensorFlow, Keras, and Theano to facilitate the development process.', 'Tensors in TensorFlow provide a compact way of storing and handling data during computation, especially beneficial for deep learning training processes with large data.', 'In a TensorFlow program, computation occurs in the form of graphs, which are prepared and then executed in a session, representing a data flow graph and enabling execution on CPUs, GPUs, or in a distributed manner.', 'Constants in TensorFlow cannot be changed during computation and are defined using tf.constant.', 'Variables in TensorFlow can be updated with different values and are defined using tf.variable.', 'Placeholders in TensorFlow are used for feeding data from external sources and are particularly useful for handling memory when dealing with large datasets.', 'Creating a graph and session objects in TensorFlow precede the execution of computations, with each variable or computation being considered an operation or a node within the graph.', 'The chapter emphasizes the transition to a different programming style in TensorFlow, requiring familiarity with its terminology and operation-based approach.', 'The practical use case implementation using TensorFlow is also mentioned, requiring some basic machine learning concepts for understanding.', 'The need to initialize variables and execute specific operations before running the graph in a session to avoid errors. It is essential to initialize variables and execute specific operations, such as tf.global_variables_initializer and session.run, before running the graph in a session to prevent errors.', 'Creating a session object by assigning a variable to TF dot session is the easiest way for beginners. This method is recommended for beginners and provides the simplest way to create a session object.', "The process of importing TensorFlow and creating variables and constants is explained. The chapter discusses the process of importing TensorFlow and creating variables named 'zero' and 'one' as well as constants using tf.variable and tf.constant methods.", 'Demonstrates the concept of placeholders in action by performing computations using the populated placeholder values, showcasing the flexibility and usage of placeholders in TensorFlow.', 'Placeholders are populated using dictionaries in TensorFlow, with examples of feeding scalar and multi-dimensional arrays.', 'Placeholder declaration: tf.placeholder is used to create a placeholder with a specified type, such as tf.float32 for floating point values.', "Using a 'with' block to create a session allows for automatic session closure, simplifying session management.", 'The importance of explicitly closing sessions to release resources and avoid resource blocking is emphasized.', 'The chapter introduces the concept of placeholders and explains the provision for feeding data into the program in chunks.', 'It details the creation of a graph with operations involving constants, addition, and multiplication.', 'It emphasizes the process of executing operations within the graph by creating a session and running specific operations.', 'The chapter explains the structure of TensorFlow programming, including the creation of variables, constants, and placeholders, as well as the execution of a graph. TensorFlow is used for machine learning, and the high-level APIs like estimator API are employed to create a linear classifier for a classification problem with real census data.', "The example involves a classification problem using real census data, where the goal is to classify whether a person's income is above 50k or below 50k, utilizing the label data available and aiming to measure the accuracy of the model.", 'Regular Python libraries such as NumPy, scikit-learn, and pandas are utilized in TensorFlow programming to prepare and format the data before performing machine learning or deep learning activities.', 'The data is imported from a CSV file named census_data.csv and the income bracket column is targeted for conversion into binary values. The chapter starts with importing data from a CSV file named census_data.csv and focuses on converting the income bracket column into binary values.', "The data is split into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing. The chapter demonstrates splitting the data into test and training sets using scikit-learn's built-in functionality, with 70% of the data allocated to training and 30% to testing.", 'The model achieved an accuracy of almost 85% using scikit learn.', 'The process of creating feature columns for categorical and continuous values, including methods such as tf.feature_column.categorical_column_with_vocabulary_list and tf.feature_column.numeric_column.', 'The usage of input functions with batch size and epochs for training, where batch size determines the number of records read at a time and epochs indicate the number of times the entire data is passed through the model for training.', 'Discussed ways to increase accuracy through training iterations and using different classifiers.', 'The creation of linear classifier using tf.estimator.LinearClassifier and the training of the model with a specified number of iterations, such as 5000 steps.', 'Provided an overview of TensorFlow, its elements, and how to write a TensorFlow program.']}