Follow along with hands-on coding to discover deep learning from scratch

Tackle different neural network models using the latest frameworks

Take advantage of years of online research to learn TensorFlow 2 efficiently

Part 1 Introduction to AI

1. Introduction

2. History of Neural Networks

3. Characteristics of Deep Learning

4. Applications of Deep Learning

6. Installation of Development Environment

2. Regression

2.1 Neuron Model

2.2 Optimization Methods

2.3 Hands-on Linear Models

2.4 Linear Regression

3. Classification

3.1 Hand-writing Digital Picture Dataset

3.2 Build a Classification Model

3.3 Compute the Error

3.4 Is the Problem Solved?

3.5 Nonlinear Model

3.6 Model Representation Ability

3.7 Optimization Method

3.9 Summary

Part 2 Tensorflow

4. Tensorflow 2 Basics

4.1 Datatype

4.2 Numerical Precision

4.3 What is a Tensor?

4.5 Applications of Tensors

4.6 Indexing and Slicing

4.7 Dimension Change

4.8 Broadcasting

4.9 Mathematical Operations

4.10 Hands-on Forward Propagation Algorithm

5. Tensorflow 2 Pro

5.1 Aggregation and Seperation

5.2 Data Statistics

5.3 Tensor Comparison

5.4 Fill and Copy

5.5 Data Clipping

5.6 High-level Operations

5.7 Load Classic Datasets

Part 3 Neural Networks

6. Neural Network Introduction

6.1 Perception Model

6.2 Fully-Connected Layers

6.3 Neural Networks

6.4 Activation Functions

6.6 Error Calculation

6.7 Neural Network Categories

6.8 Hands-on Gas Consuming Prediction

7. Backpropagation Algorithm

7.1 Derivative and Gradient

7.2 Common Properties of Derivatives

7.4 Gradient of Loss Function

7.5 Gradient of Fully-Connected Layers

7.6 Chain Rule

7.7 Back Propagation Algorithm

7.8 Hands-on Himmelblau Function Optimization

7.9 Hands-on Back Propagation Algorithm

8. Keras Basics

8.1 Basic Functionality

8.2 Model Configuration, Training and Testing

8.3 Save and Load Models

8.4 Customized Class

8.5 Model Zoo

8.6 Metrics

8.7 Visualization

9.1 Model Capability

9.2 Overfitting and Underfitting

9.3 Split the Dataset

9.4 Model Design

9.5 Regularization

9.6 Dropout

9.7 Data Enhancement

9.8 Hands-on Overfitting

Part 4 Deep Learning Applications

10.1 Problem of Fully-Connected Layers

10.2 Convolutional Neural Network

10.3 Convolutional Layer

10.4 Hands-on LeNet-5

10.5 Representation Learning

10.6 Gradient Propagation

10.8 BatchNorm Layer

10.9 Classical Convolutional Neural Network

10.10 Hands-on CIFRA10 and VGG13

10.11 Variations of Convolutional Neural Network

10.12 Deep Residual Network

10.13 DenseNet

10.14 Hands-on CIFAR10 and ResNet18

11.1 Time Series

11.2 Recurrent Neural Network (RNN)

11.3 Gradient Propagation

11.4 RNN Layer

11.5 Hands-on RNN Sentiment Classification

11.6 Gradient Vanishing and Exploding

11.8 LSTM Principle

11.9 LSTM Layer

11.10 GRU Basics

11.11 Hands-on Sentiment Classification with LSTM/GRU

11.12 Pre-trained Word Vectors

12. Auto-Encoders

12.2 Hands-on Reconstructing MNIST Pictures

12.3 Variations of Auto-Encoders

12.4 Variational Auto-Encoders (VAE)

12.5 Hands-on VAE

13.1 Examples of Game Theory

13.2 GAN Basics

13.3 Hands-on DCGAN

13.4 Variants of GAN

13.5 Nash Equilibrium

13.6 Difficulty of Training GAN

13.7 WGAN Principle

13.8 Hands-on WGAN-GP

14. Reinforcement Learning

14.1 Introduction

14.2 Reinforcement Learning Problem

14.4 Metric Function Method

14.5 Actor-Critic Method

14.6 Summary

15. Custom Dataset Pipeline

15.1 Pokémon Go Dataset

15.2 Load Customized Dataset

15.3 Hands-on Pokémon Go Dataset

15.4 Transfer Learning

15.5 Save Model

15.6 Model Deployment

Audience: Beginner to Intermediate