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Урок 1.
00:04:04
Introduction
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Урок 2.
00:12:48
Outline
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Урок 3.
00:08:27
Where to get the code
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Урок 4.
00:12:33
Intro to Google Colab, how to use a GPU or TPU for free
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Урок 5.
00:07:55
Tensorflow 2.0 in Google Colab
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Урок 6.
00:11:42
Uploading your own data to Google Colab
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Урок 7.
00:08:55
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
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Урок 8.
00:05:52
How to Succeed in this Course
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Урок 9.
00:14:27
What is Machine Learning?
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Урок 10.
00:16:00
Code Preparation (Classification Theory)
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Урок 11.
00:04:39
Beginner's Code Preamble
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Урок 12.
00:08:41
Classification Notebook
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Урок 13.
00:07:20
Code Preparation (Regression Theory)
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Урок 14.
00:10:35
Regression Notebook
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Урок 15.
00:09:59
The Neuron
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Урок 16.
00:10:55
How does a model "learn"?
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Урок 17.
00:06:46
Making Predictions
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Урок 18.
00:04:29
Saving and Loading a Model
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Урок 19.
00:04:28
Why Keras?
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Урок 20.
00:03:04
Suggestion Box
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Урок 21.
00:06:01
Artificial Neural Networks Section Introduction
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Урок 22.
00:09:41
Forward Propagation
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Урок 23.
00:09:44
The Geometrical Picture
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Урок 24.
00:17:19
Activation Functions
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Урок 25.
00:08:42
Multiclass Classification
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Урок 26.
00:12:37
How to Represent Images
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Урок 27.
00:12:43
Code Preparation (ANN)
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Урок 28.
00:08:37
ANN for Image Classification
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Урок 29.
00:11:06
ANN for Regression
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Урок 30.
00:16:39
What is Convolution? (part 1)
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Урок 31.
00:05:57
What is Convolution? (part 2)
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Урок 32.
00:06:42
What is Convolution? (part 3)
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Урок 33.
00:15:59
Convolution on Color Images
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Урок 34.
00:20:59
CNN Architecture
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Урок 35.
00:15:14
CNN Code Preparation
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Урок 36.
00:06:47
CNN for Fashion MNIST
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Урок 37.
00:04:29
CNN for CIFAR-10
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Урок 38.
00:08:52
Data Augmentation
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Урок 39.
00:05:15
Batch Normalization
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Урок 40.
00:10:23
Improving CIFAR-10 Results
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Урок 41.
00:18:28
Sequence Data
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Урок 42.
00:10:36
Forecasting
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Урок 43.
00:12:02
Autoregressive Linear Model for Time Series Prediction
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Урок 44.
00:04:13
Proof that the Linear Model Works
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Урок 45.
00:21:35
Recurrent Neural Networks
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Урок 46.
00:05:51
RNN Code Preparation
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Урок 47.
00:11:12
RNN for Time Series Prediction
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Урок 48.
00:08:28
Paying Attention to Shapes
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Урок 49.
00:17:36
GRU and LSTM (pt 1)
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Урок 50.
00:11:37
GRU and LSTM (pt 2)
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Урок 51.
00:09:20
A More Challenging Sequence
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Урок 52.
00:19:27
Demo of the Long Distance Problem
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Урок 53.
00:04:42
RNN for Image Classification (Theory)
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Урок 54.
00:04:01
RNN for Image Classification (Code)
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Урок 55.
00:12:04
Stock Return Predictions using LSTMs (pt 1)
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Урок 56.
00:05:46
Stock Return Predictions using LSTMs (pt 2)
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Урок 57.
00:12:00
Stock Return Predictions using LSTMs (pt 3)
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Урок 58.
00:05:15
Other Ways to Forecast
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Урок 59.
00:13:13
Embeddings
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Урок 60.
00:13:18
Code Preparation (NLP)
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Урок 61.
00:05:31
Text Preprocessing
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Урок 62.
00:08:20
Text Classification with LSTMs
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Урок 63.
00:08:08
CNNs for Text
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Урок 64.
00:06:11
Text Classification with CNNs
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Урок 65.
00:13:11
Recommender Systems with Deep Learning Theory
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Урок 66.
00:09:18
Recommender Systems with Deep Learning Code
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Урок 67.
00:08:13
Transfer Learning Theory
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Урок 68.
00:05:42
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
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Урок 69.
00:07:04
Large Datasets and Data Generators
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Урок 70.
00:04:52
2 Approaches to Transfer Learning
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Урок 71.
00:10:50
Transfer Learning Code (pt 1)
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Урок 72.
00:08:13
Transfer Learning Code (pt 2)
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Урок 73.
00:15:52
GAN Theory
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Урок 74.
00:12:11
GAN Code
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Урок 75.
00:06:35
Deep Reinforcement Learning Section Introduction
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Урок 76.
00:20:19
Elements of a Reinforcement Learning Problem
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Урок 77.
00:09:25
States, Actions, Rewards, Policies
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Урок 78.
00:10:08
Markov Decision Processes (MDPs)
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Урок 79.
00:04:57
The Return
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Урок 80.
00:09:54
Value Functions and the Bellman Equation
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Урок 81.
00:07:19
What does it mean to “learn”?
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Урок 82.
00:09:50
Solving the Bellman Equation with Reinforcement Learning (pt 1)
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Урок 83.
00:12:02
Solving the Bellman Equation with Reinforcement Learning (pt 2)
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Урок 84.
00:06:10
Epsilon-Greedy
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Урок 85.
00:14:16
Q-Learning
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Урок 86.
00:14:06
Deep Q-Learning / DQN (pt 1)
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Урок 87.
00:10:26
Deep Q-Learning / DQN (pt 2)
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Урок 88.
00:05:58
How to Learn Reinforcement Learning
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Урок 89.
00:05:15
Reinforcement Learning Stock Trader Introduction
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Урок 90.
00:12:23
Data and Environment
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Урок 91.
00:05:41
Replay Buffer
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Урок 92.
00:06:57
Program Design and Layout
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Урок 93.
00:05:47
Code pt 1
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Урок 94.
00:09:41
Code pt 2
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Урок 95.
00:06:28
Code pt 3
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Урок 96.
00:07:26
Code pt 4
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Урок 97.
00:03:37
Reinforcement Learning Stock Trader Discussion
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Урок 98.
00:08:20
Help! Why is the code slower on my machine?
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Урок 99.
00:05:56
What is a Web Service? (Tensorflow Serving pt 1)
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Урок 100.
00:16:57
Tensorflow Serving pt 2
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Урок 101.
00:08:31
Tensorflow Lite (TFLite)
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Урок 102.
00:08:48
Why is Google the King of Distributed Computing?
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Урок 103.
00:07:01
Training with Distributed Strategies
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Урок 104.
00:10:03
Differences Between Tensorflow 1.x and Tensorflow 2.x
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Урок 105.
00:09:40
Constants and Basic Computation
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Урок 106.
00:13:00
Variables and Gradient Tape
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Урок 107.
00:10:48
Build Your Own Custom Model
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Урок 108.
00:09:12
Mean Squared Error
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Урок 109.
00:05:59
Binary Cross Entropy
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Урок 110.
00:08:07
Categorical Cross Entropy
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Урок 111.
00:07:53
Gradient Descent
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Урок 112.
00:04:37
Stochastic Gradient Descent
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Урок 113.
00:06:11
Momentum
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Урок 114.
00:11:46
Variable and Adaptive Learning Rates
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Урок 115.
00:13:16
Adam (pt 1)
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Урок 116.
00:11:15
Adam (pt 2)
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Урок 117.
00:17:31
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
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Урок 118.
00:20:21
Anaconda Environment Setup
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Урок 119.
00:22:16
Installing NVIDIA GPU-Accelerated Deep Learning Libraries on your Home Computer
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Урок 120.
00:15:55
How to Code Yourself (part 1)
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Урок 121.
00:09:24
How to Code Yourself (part 2)
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Урок 122.
00:12:30
Proof that using Jupyter Notebook is the same as not using it
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Урок 123.
00:10:04
Is Theano Dead?
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Урок 124.
00:10:25
How to Succeed in this Course (Long Version)
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Урок 125.
00:22:05
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
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Урок 126.
00:11:19
Machine Learning and AI Prerequisite Roadmap (pt 1)
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Урок 127.
00:16:08
Machine Learning and AI Prerequisite Roadmap (pt 2)
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Урок 128.
00:02:49
What is the Appendix?
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Урок 129.
00:05:32
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