Видео курса

  • Урок 1. 00:02:58
    Updates on Udemy Reviews
  • Урок 2. 00:12:35
    What is Deep Learning?
  • Урок 3. 00:07:28
    Installing Python
  • Урок 4. 00:01:33
    How to get the dataset
  • Урок 5. 00:02:53
    Plan of Attack
  • Урок 6. 00:16:16
    The Neuron
  • Урок 7. 00:08:30
    The Activation Function
  • Урок 8. 00:12:49
    How do Neural Networks work?
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    How do Neural Networks learn?
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    Gradient Descent
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    Stochastic Gradient Descent
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    Backpropagation
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    How to get the dataset
  • Урок 14. 00:05:00
    Business Problem Description
  • Урок 15. 00:12:41
    Building an ANN - Step 1
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    Building an ANN - Step 2
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    Building an ANN - Step 3
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    Building an ANN - Step 4
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    Building an ANN - Step 5
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    Building an ANN - Step 6
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    Building an ANN - Step 7
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    Building an ANN - Step 8
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    Building an ANN - Step 9
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    Building an ANN - Step 10
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    Homework Solution
  • Урок 26. 00:19:36
    Evaluating the ANN
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    Improving the ANN
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    Tuning the ANN
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    Plan of attack
  • Урок 30. 00:15:50
    What are convolutional neural networks?
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    Step 1 - Convolution Operation
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    Step 1(b) - ReLU Layer
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    Step 2 - Pooling
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    Step 3 - Flattening
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    Step 4 - Full Connection
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    Summary
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    Softmax & Cross-Entropy
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    How to get the dataset
  • Урок 39. 00:04:09
    Introduction to CNNs
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    Building a CNN - Step 1
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    Building a CNN - Step 2
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    Building a CNN - Step 3
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    Building a CNN - Step 4
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    Building a CNN - Step 5
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    Building a CNN - Step 6
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    Building a CNN - Step 7
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    Building a CNN - Step 8
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    Building a CNN - Step 9
  • Урок 49. 00:08:26
    Building a CNN - Step 10
  • Урок 50. 00:16:05
    Homework Solution
  • Урок 51. 00:02:33
    Plan of attack
  • Урок 52. 00:16:03
    The idea behind Recurrent Neural Networks
  • Урок 53. 00:14:28
    The Vanishing Gradient Problem
  • Урок 54. 00:19:48
    LSTMs
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    Practical intuition
  • Урок 56. 00:03:38
    EXTRA: LSTM Variations
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    How to get the dataset
  • Урок 58. 00:06:30
    Building a RNN - Step 1
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    Building a RNN - Step 2
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    Building a RNN - Step 3
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    Building a RNN - Step 4
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    Building a RNN - Step 5
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    Building a RNN - Step 6
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    Building a RNN - Step 7
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    Building a RNN - Step 8
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    Building a RNN - Step 9
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    Building a RNN - Step 10
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    Building a RNN - Step 11
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    Building a RNN - Step 12
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    Building a RNN - Step 13
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    Building a RNN - Step 14
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    Building a RNN - Step 15
  • Урок 73. 00:03:11
    Plan of attack
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    How do Self-Organizing Maps Work?
  • Урок 75. 00:02:20
    Why revisit K-Means?
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    K-Means Clustering (Refresher)
  • Урок 77. 00:14:25
    How do Self-Organizing Maps Learn? (Part 1)
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    How do Self-Organizing Maps Learn? (Part 2)
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    Live SOM example
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    Reading an Advanced SOM
  • Урок 81. 00:07:49
    EXTRA: K-means Clustering (part 2)
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    EXTRA: K-means Clustering (part 3)
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    How to get the dataset
  • Урок 84. 00:13:43
    Building a SOM - Step 1
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    Building a SOM - Step 2
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    Building a SOM - Step 3
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    Building a SOM - Step 4
  • Урок 88. 00:02:50
    Mega Case Study - Step 1
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    Mega Case Study - Step 2
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    Mega Case Study - Step 3
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    Mega Case Study - Step 4
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    Plan of attack
  • Урок 93. 00:14:23
    Boltzmann Machine
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    Energy-Based Models (EBM)
  • Урок 95. 00:03:29
    Editing Wikipedia - Our Contribution to the World
  • Урок 96. 00:17:30
    Restricted Boltzmann Machine
  • Урок 97. 00:16:29
    Contrastive Divergence
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    Deep Belief Networks
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    Deep Boltzmann Machines
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    How to get the dataset
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    Building a Boltzmann Machine - Introduction
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    Building a Boltzmann Machine - Step 1
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    Building a Boltzmann Machine - Step 2
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    Building a Boltzmann Machine - Step 3
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    Building a Boltzmann Machine - Step 4
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    Building a Boltzmann Machine - Step 5
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    Building a Boltzmann Machine - Step 6
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    Building a Boltzmann Machine - Step 7
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    Building a Boltzmann Machine - Step 8
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    Building a Boltzmann Machine - Step 9
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    Building a Boltzmann Machine - Step 10
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    Building a Boltzmann Machine - Step 11
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    Building a Boltzmann Machine - Step 12
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    Building a Boltzmann Machine - Step 13
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    Building a Boltzmann Machine - Step 14
  • Урок 116. 00:02:13
    Plan of attack
  • Урок 117. 00:10:51
    Auto Encoders
  • Урок 118. 00:01:16
    A Note on Biases
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    Training an Auto Encoder
  • Урок 120. 00:03:53
    Overcomplete hidden layers
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    Sparse Autoencoders
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    Denoising Autoencoders
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    Contractive Autoencoders
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    Stacked Autoencoders
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    Deep Autoencoders
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    How to get the dataset
  • Урок 127. 00:12:05
    Building an AutoEncoder - Step 1
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    Building an AutoEncoder - Step 2
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    Building an AutoEncoder - Step 3
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    Building an AutoEncoder - Step 4
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    Building an AutoEncoder - Step 5
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    Building an AutoEncoder - Step 6
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    Building an AutoEncoder - Step 7
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    Building an AutoEncoder - Step 8
  • Урок 135. 00:13:33
    Building an AutoEncoder - Step 9
  • Урок 136. 00:04:23
    Building an AutoEncoder - Step 10
  • Урок 137. 00:11:27
    Building an AutoEncoder - Step 11
  • Урок 138. 00:02:41
    THANK YOU bonus video
  • Урок 139. 00:05:46
    Simple Linear Regression Intuition - Step 1
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    Simple Linear Regression Intuition - Step 2
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    Multiple Linear Regression Intuition
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    Logistic Regression Intuition
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    Data Preprocessing - Step 1
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    Data Preprocessing - Step 2
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    Data Preprocessing - Step 3
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    Data Preprocessing - Step 4
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    Data Preprocessing - Step 5
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    Data Preprocessing - Step 6
  • Урок 149. 00:03:42
    Data Preprocessing Template
  • Урок 150. 00:05:22
    Logistic Regression Implementation - Step 1
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    Logistic Regression Implementation - Step 2
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    Logistic Regression Implementation - Step 3
  • Урок 153. 00:04:14
    Logistic Regression Implementation - Step 4
  • Урок 154. 00:19:35
    Logistic Regression Implementation - Step 5
  • Урок 155. 00:03:40
    Classification Template
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