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Tensorflow 2.0 Deep Learning and Artificial Intelligence


Welcome :
Introduction
Outline
Where to get the code

Google Colab :
Intro to Google Colab, how to use a GPU or TPU for free
Tensorflow 2.0 in Google Colab
Uploading your own data to Google Colab
Where can I learn about Numpy, Scipy, Matplotlib, Pandas, and Scikit-Learn?

Machine Learning and Neurons :
What is Machine Learning?
Code Preparation (Classification Theory)
Classification Notebook
Code Preparation (Regression Theory)
Regression Notebook
The Neuron
How does a model "learn"?
Making Predictions
Saving and Loading a Model

Feedforward Artificial Neural Networks :
Artificial Neural Networks Section Introduction
Forward Propagation
The Geometrical Picture
Activation Functions
Multiclass Classification
How to Represent Images
Code Preparation (ANN)
ANN for Image Classification
ANN for Regression

Convolutional Neural Networks :
What is Convolution? (part 1)
What is Convolution? (part 2)
What is Convolution? (part 3)
Convolution on Color Images
CNN Architecture
CNN Code Preparation
CNN for Fashion MNIST
CNN for CIFAR-10
Data Augmentation
Batch Normalization
Improving CIFAR-10 Results

Recurrent Neural Networks, Time Series, and Sequence Data :
Sequence Data
Forecasting
Autoregressive Linear Model for Time Series Prediction
Proof that the Linear Model Works
Recurrent Neural Networks
RNN Code Preparation
RNN for Time Series Prediction
Paying Attention to Shapes
GRU and LSTM (pt 1)
GRU and LSTM (pt 2)
A More Challenging Sequence
Demo of the Long Distance Problem
RNN for Image Classification (Theory)
RNN for Image Classification (Code)
Stock Return Predictions using LSTMs (pt 1)
Stock Return Predictions using LSTMs (pt 2)
Stock Return Predictions using LSTMs (pt 3)

Natural Language Processing (NLP) :
Embeddings
Code Preparation (NLP)
Text Preprocessing
Text Classification with LSTMs
CNNs for Text
Text Classification with CNNs

Recommender Systems :
Recommender Systems with Deep Learning Theory
Recommender Systems with Deep Learning Code

Transfer Learning for Computer Vision :
Transfer Learning Theory
Some Pre-trained Models (VGG, ResNet, Inception, MobileNet)
Large Datasets and Data Generators
2 Approaches to Transfer Learning
Transfer Learning Code (pt 1)
Transfer Learning Code (pt 2)

GANs (Generative Adversarial Networks) :
GAN Theory
GAN Code

Deep Reinforcement Learning (Theory) :
Deep Reinforcement Learning Section Introduction
Elements of a Reinforcement Learning Problem
States, Actions, Rewards, Policies
Markov Decision Processes (MDPs)
The Return
Value Functions and the Bellman Equation
What does it mean to “learn”?
Solving the Bellman Equation with Reinforcement Learning (pt 1)
Solving the Bellman Equation with Reinforcement Learning (pt 2)
Epsilon-Greedy
Q-Learning
Deep Q-Learning / DQN (pt 1)
Deep Q-Learning / DQN (pt 2)
How to Learn Reinforcement Learning

Stock Trading Project with Deep Reinforcement Learning :
Reinforcement Learning Stock Trader Introduction
Data and Environment
Replay Buffer
Program Design and Layout
Code pt 1
Code pt 2
Code pt 3
Code pt 4
Reinforcement Learning Stock Trader Discussion

Advanced Tensorflow Usage :
What is a Web Service? (Tensorflow Serving pt 1)
Tensorflow Serving pt 2
Tensorflow Lite (TFLite)
Why is Google the King of Distributed Computing?
Training with Distributed Strategies
Using the TPU

Low-Level Tensorflow :
Differences Between Tensorflow 1.x and Tensorflow 2.x
Constants and Basic Computation
Variables and Gradient Tape
Build Your Own Custom Model

VIP: DeepDream :
DeepDream Theory
DeepDream Code Outline (pt 1)
DeepDream Code (pt 1)
DeepDream Code Outline (pt 2)
DeepDream Code (pt 2)
DeepDream Code Outline (pt 3)
DeepDream Code (pt 3)

In-Depth: Loss Functions :
Mean Squared Error
Binary Cross Entropy
Categorical Cross Entropy

In-Depth: Gradient Descent :
Gradient Descent
Stochastic Gradient Descent
Momentum
Variable and Adaptive Learning Rates
Adam

Extras :
Links to TF2.0 Notebooks

Appendix / FAQ :
What is the Appendix?
Windows-Focused Environment Setup 2018
How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
How to Code Yourself (part 1)
How to Code Yourself (part 2)
Proof that using Jupyter Notebook is the same as not using it
How to Succeed in this Course (Long Version)
Is Theano Dead?
What order should I take your courses in? (part 1)
What order should I take your courses in? (part 2)
Bonus: Where to get discount coupons and FREE deep learning material