PyTorch: Deep Learning and Artificial Intelligence

  • Introduction
  • Welcome
Welcome
04:04
Overview and Outline
13:14
Where to get the code
02:06
Intro to Google Colab, how to use a GPU or TPU for free
12:33
Uploading your own data to Google Colab
11:42
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
08:55
Temporary 403 Errors
02:58
What is Machine Learning?
14:26
Regression Basics
14:39
Regression Code Preparation
11:45
Regression Notebook
13:14
Moore's Law
06:57
Moore's Law Notebook
13:51
Linear Regression Exercise: Real Estate Predictions
02:33
Linear Classification Basics
15:06
Classification Code Preparation
06:56
Classification Notebook
12:00
Logistic Regression Exercise: Predicting Diabetes Onset
02:34
Saving and Loading a Model
05:21
A Short Neuroscience Primer
09:51
How does a model "learn"?
10:50
Model With Logits
04:18
Train Sets vs. Validation Sets vs. Test Sets
10:12
Suggestion Box
03:10
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
09:39
How to Represent Images
12:21
Color Mixing Clarification
55:00
Code Preparation (ANN)
14:57
ANN for Image Classification
18:28
ANN for Regression
10:55
Exercise: E. Coli Protein Localization Sites
02:21
How to Choose Hyperparameters
06:17
What is Convolution? (part 1)
16:38
What is Convolution? (part 2 - Pattern Finding)
05:56
What is Convolution? (part 3 - Weight Sharing)
06:41
Convolution on Color Images
15:58
CNN Architecture
20:53
CNN Code Preparation (part 1)
17:42
CNN Code Preparation (part 2)
08:00
CNN Code Preparation (part 3)
05:40
CNN for Fashion MNIST
11:32
CNN for CIFAR-10
08:05
Data Augmentation
09:45
Batch Normalization
05:14
Improving CIFAR-10 Results
10:46
Exercise: Facial Expression Recognition
01:35
Sequence Data
22:14
Forecasting
10:58
Autoregressive Linear Model for Time Series Prediction
12:15
Proof that the Linear Model Works
04:12
Recurrent Neural Networks
21:31
RNN Code Preparation
13:49
RNN for Time Series Prediction
09:29
Paying Attention to Shapes
09:33
GRU and LSTM (pt 1)
17:35
GRU and LSTM (pt 2)
11:45
A More Challenging Sequence
10:28
RNN for Image Classification (Theory)
04:41
RNN for Image Classification (Code)
02:48
Stock Return Predictions using LSTMs (pt 1)
12:24
Stock Return Predictions using LSTMs (pt 2)
06:16
Stock Return Predictions using LSTMs (pt 3)
11:45
Other Ways to Forecast
05:14
Exercise: More Forecasting
01:52
Embeddings
13:12
Neural Networks with Embeddings
03:45
Text Preprocessing Concepts
13:33
Beginner Blues - PyTorch NLP Version
10:36
(Legacy) Text Preprocessing Code Preparation
11:53
(Legacy) Text Preprocessing Code Example
07:53
(Legacy) Text Classification with LSTMs (V1)
08:55
Text Classification with LSTMs (V2)
17:42
CNNs for Text
12:07
(Legacy) Text Classification with CNNs (V1)
04:49
Text Classification with CNNs (V2)
07:15
(Legacy) VIP: Making Predictions with a Trained NLP Model
07:37
VIP: Making Predictions with a Trained NLP Model (V2)
04:21
Exercise: Sentiment Analysis
02:01
Recommender Systems with Deep Learning Theory
10:26
Recommender Systems with Deep Learning Code Preparation
09:38
Recommender Systems with Deep Learning Code (pt 1)
08:52
Recommender Systems with Deep Learning Code (pt 2)
12:31
VIP: Making Predictions with a Trained Recommender Model
04:51
Exercise: Book Recommendations
01:13
Transfer Learning Theory
08:12
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
04:05
Large Datasets
07:11
2 Approaches to Transfer Learning
04:51
Transfer Learning Code (pt 1)
09:36
Transfer Learning Code (pt 2)
07:40
Exercise: Transfer Learning
01:28
GAN Theory
16:03
GAN Code Preparation
06:18
GAN Code
09:21
Exercise: DCGAN (Deep Convolutional GAN)
02:54
Reinforcement Learning Section Introduction
06:34
Elements of a Reinforcement Learning Problem
20:18
States, Actions, Rewards, Policies
09:24
Markov Decision Processes (MDPs)
10:07
The Return
04:56
Value Functions and the Bellman Equation
09:53
What does it mean to “learn”?
07:18
Solving the Bellman Equation with Reinforcement Learning (pt 1)
09:49
Solving the Bellman Equation with Reinforcement Learning (pt 2)
12:04
Epsilon-Greedy
06:09
Q-Learning
14:15
Deep Q-Learning / DQN (pt 1)
14:05
Deep Q-Learning / DQN (pt 2)
10:25
How to Learn Reinforcement Learning
05:56
Reinforcement Learning Stock Trader Introduction
05:13
Data and Environment
12:22
Replay Buffer
05:40
Program Design and Layout
06:56
Code pt 1
09:22
Code pt 2
09:40
Code pt 3
06:54
Code pt 4
07:25
Reinforcement Learning Stock Trader Discussion
03:36
Exercise: Personalized Stock Trading Bot
01:44
Custom Loss and Estimating Prediction Uncertainty
09:36
Estimating Prediction Uncertainty Code
07:12
Facial Recognition Section Introduction
03:39
Siamese Networks
10:17
Code Outline
05:05
Loading in the data
05:52
Splitting the data into train and test
04:27
Converting the data into pairs
05:04
Generating Generators
05:06
Creating the model and loss
04:28
Accuracy and imbalanced classes
07:48
Facial Recognition Section Summary
03:32
Mean Squared Error
09:11
Binary Cross Entropy
05:58
Categorical Cross Entropy
08:06
Gradient Descent
07:52
Stochastic Gradient Descent
04:36
Momentum
06:11
Variable and Adaptive Learning Rates
11:46
Adam Optimization (pt 1)
13:15
Adam Optimization (pt 2)
11:14
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Beginner's Coding Tips
13:22
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
Is Theano Dead?
10:04
How to use Github & Extra Coding Tips (Optional)
11:12
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49
Welcome
04:04
Overview and Outline
13:14
Where to get the code
02:06
Intro to Google Colab, how to use a GPU or TPU for free
12:33
Uploading your own data to Google Colab
11:42
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?
08:55
Temporary 403 Errors
02:58
What is Machine Learning?
14:26
Regression Basics
14:39
Regression Code Preparation
11:45
Regression Notebook
13:14
Moore's Law
06:57
Moore's Law Notebook
13:51
Linear Regression Exercise: Real Estate Predictions
02:33
Linear Classification Basics
15:06
Classification Code Preparation
06:56
Classification Notebook
12:00
Logistic Regression Exercise: Predicting Diabetes Onset
02:34
Saving and Loading a Model
05:21
A Short Neuroscience Primer
09:51
How does a model "learn"?
10:50
Model With Logits
04:18
Train Sets vs. Validation Sets vs. Test Sets
10:12
Suggestion Box
03:10
Artificial Neural Networks Section Introduction
06:00
Forward Propagation
09:40
The Geometrical Picture
09:43
Activation Functions
17:18
Multiclass Classification
09:39
How to Represent Images
12:21
Color Mixing Clarification
55:00
Code Preparation (ANN)
14:57
ANN for Image Classification
18:28
ANN for Regression
10:55
Exercise: E. Coli Protein Localization Sites
02:21
How to Choose Hyperparameters
06:17
What is Convolution? (part 1)
16:38
What is Convolution? (part 2 - Pattern Finding)
05:56
What is Convolution? (part 3 - Weight Sharing)
06:41
Convolution on Color Images
15:58
CNN Architecture
20:53
CNN Code Preparation (part 1)
17:42
CNN Code Preparation (part 2)
08:00
CNN Code Preparation (part 3)
05:40
CNN for Fashion MNIST
11:32
CNN for CIFAR-10
08:05
Data Augmentation
09:45
Batch Normalization
05:14
Improving CIFAR-10 Results
10:46
Exercise: Facial Expression Recognition
01:35
Sequence Data
22:14
Forecasting
10:58
Autoregressive Linear Model for Time Series Prediction
12:15
Proof that the Linear Model Works
04:12
Recurrent Neural Networks
21:31
RNN Code Preparation
13:49
RNN for Time Series Prediction
09:29
Paying Attention to Shapes
09:33
GRU and LSTM (pt 1)
17:35
GRU and LSTM (pt 2)
11:45
A More Challenging Sequence
10:28
RNN for Image Classification (Theory)
04:41
RNN for Image Classification (Code)
02:48
Stock Return Predictions using LSTMs (pt 1)
12:24
Stock Return Predictions using LSTMs (pt 2)
06:16
Stock Return Predictions using LSTMs (pt 3)
11:45
Other Ways to Forecast
05:14
Exercise: More Forecasting
01:52
Embeddings
13:12
Neural Networks with Embeddings
03:45
Text Preprocessing Concepts
13:33
Beginner Blues - PyTorch NLP Version
10:36
(Legacy) Text Preprocessing Code Preparation
11:53
(Legacy) Text Preprocessing Code Example
07:53
(Legacy) Text Classification with LSTMs (V1)
08:55
Text Classification with LSTMs (V2)
17:42
CNNs for Text
12:07
(Legacy) Text Classification with CNNs (V1)
04:49
Text Classification with CNNs (V2)
07:15
(Legacy) VIP: Making Predictions with a Trained NLP Model
07:37
VIP: Making Predictions with a Trained NLP Model (V2)
04:21
Exercise: Sentiment Analysis
02:01
Recommender Systems with Deep Learning Theory
10:26
Recommender Systems with Deep Learning Code Preparation
09:38
Recommender Systems with Deep Learning Code (pt 1)
08:52
Recommender Systems with Deep Learning Code (pt 2)
12:31
VIP: Making Predictions with a Trained Recommender Model
04:51
Exercise: Book Recommendations
01:13
Transfer Learning Theory
08:12
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
04:05
Large Datasets
07:11
2 Approaches to Transfer Learning
04:51
Transfer Learning Code (pt 1)
09:36
Transfer Learning Code (pt 2)
07:40
Exercise: Transfer Learning
01:28
GAN Theory
16:03
GAN Code Preparation
06:18
GAN Code
09:21
Exercise: DCGAN (Deep Convolutional GAN)
02:54
Reinforcement Learning Section Introduction
06:34
Elements of a Reinforcement Learning Problem
20:18
States, Actions, Rewards, Policies
09:24
Markov Decision Processes (MDPs)
10:07
The Return
04:56
Value Functions and the Bellman Equation
09:53
What does it mean to “learn”?
07:18
Solving the Bellman Equation with Reinforcement Learning (pt 1)
09:49
Solving the Bellman Equation with Reinforcement Learning (pt 2)
12:04
Epsilon-Greedy
06:09
Q-Learning
14:15
Deep Q-Learning / DQN (pt 1)
14:05
Deep Q-Learning / DQN (pt 2)
10:25
How to Learn Reinforcement Learning
05:56
Reinforcement Learning Stock Trader Introduction
05:13
Data and Environment
12:22
Replay Buffer
05:40
Program Design and Layout
06:56
Code pt 1
09:22
Code pt 2
09:40
Code pt 3
06:54
Code pt 4
07:25
Reinforcement Learning Stock Trader Discussion
03:36
Exercise: Personalized Stock Trading Bot
01:44
Custom Loss and Estimating Prediction Uncertainty
09:36
Estimating Prediction Uncertainty Code
07:12
Facial Recognition Section Introduction
03:39
Siamese Networks
10:17
Code Outline
05:05
Loading in the data
05:52
Splitting the data into train and test
04:27
Converting the data into pairs
05:04
Generating Generators
05:06
Creating the model and loss
04:28
Accuracy and imbalanced classes
07:48
Facial Recognition Section Summary
03:32
Mean Squared Error
09:11
Binary Cross Entropy
05:58
Categorical Cross Entropy
08:06
Gradient Descent
07:52
Stochastic Gradient Descent
04:36
Momentum
06:11
Variable and Adaptive Learning Rates
11:46
Adam Optimization (pt 1)
13:15
Adam Optimization (pt 2)
11:14
What is the Appendix?
03:47
Pre-Installation Check
04:13
Anaconda Environment Setup
20:21
How to install Numpy, Scipy, Matplotlib, Pandas, PyTorch, and TensorFlow
17:33
Beginner's Coding Tips
13:22
How to Code Yourself (part 1)
15:55
How to Code Yourself (part 2)
09:24
Proof that using Jupyter Notebook is the same as not using it
12:29
Python 2 vs Python 3
04:38
Is Theano Dead?
10:04
How to use Github & Extra Coding Tips (Optional)
11:12
How to Succeed in this Course (Long Version)
10:25
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
22:05
What order should I take your courses in? (part 1)
11:19
What order should I take your courses in? (part 2)
16:07
Where to get discount coupons and FREE deep learning material
05:49

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