• Урок 1. 00:03:05
    Introduction
  • Урок 2. 00:09:25
    Course Overview -- PLEASE DON'T SKIP THIS LECTURE! Thanks :)
  • Урок 3. 00:12:02
    Installing TensorFlow and Environment Setup
  • Урок 4. 00:17:17
    Machine Learning Overview
  • Урок 5. 00:01:13
    Crash Course Section Introduction
  • Урок 6. 00:15:33
    NumPy Crash Course
  • Урок 7. 00:04:24
    Pandas Crash Course
  • Урок 8. 00:07:42
    Data Visualization Crash Course
  • Урок 9. 00:09:05
    SciKit Learn Preprocessing Overview
  • Урок 10. 00:02:08
    Crash Course Review Exercise
  • Урок 11. 00:06:00
    Crash Course Review Exercise - Solutions
  • Урок 12. 00:01:07
    Introduction to Neural Networks
  • Урок 13. 00:05:13
    Introduction to Perceptron
  • Урок 14. 00:06:31
    Neural Network Activation Functions
  • Урок 15. 00:03:41
    Cost Functions
  • Урок 16. 00:03:21
    Gradient Descent Backpropagation
  • Урок 17. 00:08:49
    TensorFlow Playground
  • Урок 18. 00:06:18
    Manual Creation of Neural Network - Part One
  • Урок 19. 00:07:56
    Manual Creation of Neural Network - Part Two - Operations
  • Урок 20. 00:08:58
    Manual Creation of Neural Network - Part Three - Placeholders and Variables
  • Урок 21. 00:09:49
    Manual Creation of Neural Network - Part Four - Session
  • Урок 22. 00:16:29
    Manual Neural Network Classification Task
  • Урок 23. 00:01:27
    Introduction to TensorFlow
  • Урок 24. 00:12:41
    TensorFlow Basic Syntax
  • Урок 25. 00:05:49
    TensorFlow Graphs
  • Урок 26. 00:05:58
    Variables and Placeholders
  • Урок 27. 00:07:48
    TensorFlow - A Neural Network - Part One
  • Урок 28. 00:19:51
    TensorFlow - A Neural Network - Part Two
  • Урок 29. 00:19:44
    TensorFlow Regression Example - Part One
  • Урок 30. 00:22:05
    TensorFlow Regression Example _ Part Two
  • Урок 31. 00:14:01
    TensorFlow Classification Example - Part One
  • Урок 32. 00:12:47
    TensorFlow Classification Example - Part Two
  • Урок 33. 00:03:21
    TF Regression Exercise
  • Урок 34. 00:12:35
    TF Regression Exercise Solution Walkthrough
  • Урок 35. 00:04:27
    TF Classification Exercise
  • Урок 36. 00:11:28
    TF Classification Exercise Solution Walkthrough
  • Урок 37. 00:05:55
    Saving and Restoring Models
  • Урок 38. 00:00:50
    Introduction to Convolutional Neural Network Section
  • Урок 39. 00:02:33
    Review of Neural Networks
  • Урок 40. 00:14:51
    New Theory Topics
  • Урок 41. 00:04:47
    MNIST Data Overview
  • Урок 42. 00:08:30
    MNIST Basic Approach Part One
  • Урок 43. 00:16:48
    MNIST Basic Approach Part Two
  • Урок 44. 00:18:42
    CNN Theory Part One
  • Урок 45. 00:04:33
    CNN Theory Part Two
  • Урок 46. 00:17:26
    CNN MNIST Code Along - Part One
  • Урок 47. 00:06:06
    CNN MNIST Code Along - Part Two
  • Урок 48. 00:06:02
    Introduction to CNN Project
  • Урок 49. 00:15:26
    CNN Project Exercise Solution - Part One
  • Урок 50. 00:13:00
    CNN Project Exercise Solution - Part Two
  • Урок 51. 00:01:08
    Introduction to RNN Section
  • Урок 52. 00:07:58
    RNN Theory
  • Урок 53. 00:11:58
    Manual Creation of RNN
  • Урок 54. 00:04:38
    Vanishing Gradients
  • Урок 55. 00:09:50
    LSTM and GRU Theory
  • Урок 56. 00:04:39
    Introduction to RNN with TensorFlow API
  • Урок 57. 00:20:51
    RNN with TensorFlow - Part One
  • Урок 58. 00:19:01
    RNN with TensorFlow - Part Two
  • Урок 59. 00:08:02
    RNN with TensorFlow - Part Three
  • Урок 60. 00:07:04
    Time Series Exercise Overview
  • Урок 61. 00:18:18
    Time Series Exercise Solution
  • Урок 62. 00:02:50
    Quick Note on Word2Vec
  • Урок 63. 00:12:03
    Word2Vec Theory
  • Урок 64. 00:16:48
    Word2Vec Code Along - Part One
  • Урок 65. 00:13:12
    Word2Vec Part Two
  • Урок 66. 00:07:13
    Deep Nets with Tensorflow Abstractions API - Part One
  • Урок 67. 00:07:26
    Deep Nets with Tensorflow Abstractions API - Estimator API
  • Урок 68. 00:11:56
    Deep Nets with Tensorflow Abstractions API - Keras
  • Урок 69. 00:11:03
    Deep Nets with Tensorflow Abstractions API - Layers
  • Урок 70. 00:16:08
    Tensorboard
  • Урок 71. 00:07:58
    Autoencoder Basics
  • Урок 72. 00:17:26
    Dimensionality Reduction with Linear Autoencoder
  • Урок 73. 00:01:45
    Linear Autoencoder PCA Exercise Overview
  • Урок 74. 00:07:52
    Linear Autoencoder PCA Exercise Solutions
  • Урок 75. 00:19:34
    Stacked Autoencoder
  • Урок 76. 00:04:19
    Introduction to Reinforcement Learning with OpenAI Gym
  • Урок 77. 00:05:38
    Introduction to OpenAI Gym
  • Урок 78. 00:07:20
    OpenAI Gym Steup
  • Урок 79. 00:05:42
    Open AI Gym Env Basics
  • Урок 80. 00:08:06
    Open AI Gym Observations
  • Урок 81. 00:08:03
    OpenAI Gym Actions
  • Урок 82. 00:16:21
    Simple Neural Network Game
  • Урок 83. 00:07:40
    Policy Gradient Theory
  • Урок 84. 00:11:26
    Policy Gradient Code Along Part One
  • Урок 85. 00:12:23
    Policy Gradient Code Along Part Two
  • Урок 86. 00:07:14
    Introduction to GANs
  • Урок 87. 00:09:07
    GAN Code Along - Part One
  • Урок 88. 00:11:27
    GAN Code Along - Part Two
  • Урок 89. 00:11:56
    GAN Code Along - Part Three
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