1. Урок 1.00:03:05
    Introduction
  2. Урок 2.00:09:25
    Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
  3. Урок 3.00:12:02
    Installing TensorFlow and Environment Setup
  4. Урок 4.00:17:17
    Machine Learning Overview
  5. Урок 5.00:01:13
    Crash Course Section Introduction
  6. Урок 6.00:15:33
    NumPy Crash Course
  7. Урок 7.00:04:24
    Pandas Crash Course
  8. Урок 8.00:07:42
    Data Visualization Crash Course
  9. Урок 9.00:09:05
    SciKit Learn Preprocessing Overview
  10. Урок 10.00:02:08
    Crash Course Review Exercise
  11. Урок 11.00:06:00
    Crash Course Review Exercise - Solutions
  12. Урок 12.00:01:07
    Introduction to Neural Networks
  13. Урок 13.00:05:13
    Introduction to Perceptron
  14. Урок 14.00:06:31
    Neural Network Activation Functions
  15. Урок 15.00:03:41
    Cost Functions
  16. Урок 16.00:03:21
    Gradient Descent Backpropagation
  17. Урок 17.00:08:49
    TensorFlow Playground
  18. Урок 18.00:06:18
    Manual Creation of Neural Network - Part One
  19. Урок 19.00:07:56
    Manual Creation of Neural Network - Part Two - Operations
  20. Урок 20.00:08:58
    Manual Creation of Neural Network - Part Three - Placeholders and Variables
  21. Урок 21.00:09:49
    Manual Creation of Neural Network - Part Four - Session
  22. Урок 22.00:16:29
    Manual Neural Network Classification Task
  23. Урок 23.00:01:27
    Introduction to TensorFlow
  24. Урок 24.00:12:41
    TensorFlow Basic Syntax
  25. Урок 25.00:05:49
    TensorFlow Graphs
  26. Урок 26.00:05:58
    Variables and Placeholders
  27. Урок 27.00:07:48
    TensorFlow - A Neural Network - Part One
  28. Урок 28.00:19:51
    TensorFlow - A Neural Network - Part Two
  29. Урок 29.00:19:44
    TensorFlow Regression Example - Part One
  30. Урок 30.00:22:05
    TensorFlow Regression Example _ Part Two
  31. Урок 31.00:14:01
    TensorFlow Classification Example - Part One
  32. Урок 32.00:12:47
    TensorFlow Classification Example - Part Two
  33. Урок 33.00:03:21
    TF Regression Exercise
  34. Урок 34.00:12:35
    TF Regression Exercise Solution Walkthrough
  35. Урок 35.00:04:27
    TF Classification Exercise
  36. Урок 36.00:11:28
    TF Classification Exercise Solution Walkthrough
  37. Урок 37.00:05:55
    Saving and Restoring Models
  38. Урок 38.00:00:50
    Introduction to Convolutional Neural Network Section
  39. Урок 39.00:02:33
    Review of Neural Networks
  40. Урок 40.00:14:51
    New Theory Topics
  41. Урок 41.00:04:47
    MNIST Data Overview
  42. Урок 42.00:08:30
    MNIST Basic Approach Part One
  43. Урок 43.00:16:48
    MNIST Basic Approach Part Two
  44. Урок 44.00:18:42
    CNN Theory Part One
  45. Урок 45.00:04:33
    CNN Theory Part Two
  46. Урок 46.00:17:26
    CNN MNIST Code Along - Part One
  47. Урок 47.00:06:06
    CNN MNIST Code Along - Part Two
  48. Урок 48.00:06:02
    Introduction to CNN Project
  49. Урок 49.00:15:26
    CNN Project Exercise Solution - Part One
  50. Урок 50.00:13:00
    CNN Project Exercise Solution - Part Two
  51. Урок 51.00:01:08
    Introduction to RNN Section
  52. Урок 52.00:07:58
    RNN Theory
  53. Урок 53.00:11:58
    Manual Creation of RNN
  54. Урок 54.00:04:38
    Vanishing Gradients
  55. Урок 55.00:09:50
    LSTM and GRU Theory
  56. Урок 56.00:04:39
    Introduction to RNN with TensorFlow API
  57. Урок 57.00:20:51
    RNN with TensorFlow - Part One
  58. Урок 58.00:19:01
    RNN with TensorFlow - Part Two
  59. Урок 59.00:08:02
    RNN with TensorFlow - Part Three
  60. Урок 60.00:07:04
    Time Series Exercise Overview
  61. Урок 61.00:18:18
    Time Series Exercise Solution
  62. Урок 62.00:02:50
    Quick Note on Word2Vec
  63. Урок 63.00:12:03
    Word2Vec Theory
  64. Урок 64.00:16:48
    Word2Vec Code Along - Part One
  65. Урок 65.00:13:12
    Word2Vec Part Two
  66. Урок 66.00:07:13
    Deep Nets with Tensorflow Abstractions API - Part One
  67. Урок 67.00:07:26
    Deep Nets with Tensorflow Abstractions API - Estimator API
  68. Урок 68.00:11:56
    Deep Nets with Tensorflow Abstractions API - Keras
  69. Урок 69.00:11:03
    Deep Nets with Tensorflow Abstractions API - Layers
  70. Урок 70.00:16:08
    Tensorboard
  71. Урок 71.00:07:58
    Autoencoder Basics
  72. Урок 72.00:17:26
    Dimensionality Reduction with Linear Autoencoder
  73. Урок 73.00:01:45
    Linear Autoencoder PCA Exercise Overview
  74. Урок 74.00:07:52
    Linear Autoencoder PCA Exercise Solutions
  75. Урок 75.00:19:34
    Stacked Autoencoder
  76. Урок 76.00:04:19
    Introduction to Reinforcement Learning with OpenAI Gym
  77. Урок 77.00:05:38
    Introduction to OpenAI Gym
  78. Урок 78.00:07:20
    OpenAI Gym Steup
  79. Урок 79.00:05:42
    Open AI Gym Env Basics
  80. Урок 80.00:08:06
    Open AI Gym Observations
  81. Урок 81.00:08:03
    OpenAI Gym Actions
  82. Урок 82.00:16:21
    Simple Neural Network Game
  83. Урок 83.00:07:40
    Policy Gradient Theory
  84. Урок 84.00:11:26
    Policy Gradient Code Along Part One
  85. Урок 85.00:12:23
    Policy Gradient Code Along Part Two
  86. Урок 86.00:07:14
    Introduction to GANs
  87. Урок 87.00:09:07
    GAN Code Along - Part One
  88. Урок 88.00:11:27
    GAN Code Along - Part Two
  89. Урок 89.00:11:56
    GAN Code Along - Part Three