You''ll start with an introduction to AI, where you''ll learn the history of neural networks and what sets deep learning apart from other varieties of machine learning. Discovery the variety of deep learning frameworks and set-up a deep learning development environment. Next, you''ll jump into simple classification programs for hand-writing analysis. Once you''ve tackled the basics of deep learning, you move on to TensorFlow 2 specifically. Find out what exactly a Tensor is and how to work with MNIST datasets. Finally, you''ll get into the heavy lifting of programming neural networks and working with a wide variety of neural network types such as GANs
Table of ContentsPart 1 Introduction to AI
1. Introduction
1. Artificial Intelligence
2. History of Neural Networks
3. Characteristics of Deep Learning
4. Applications of Deep Learning
5. Deep Learning Frameworks
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
3.8 Hands-on Hand-written Recognition
3.9 Summary
Part 2 Tensorflow
4. Tensorflow 2 Basics
4.1 Datatype
4.2 Numerical Precision
4.3 What is a Tensor?
4.4 Create 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
5.8 Hands-on MNIST Dataset Practice
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
6.5 Output Layer
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
7.3 Derivatives of Activation Functions
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. Overfitting
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. Convolutional Neural Network
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
10.7 Pooling Layer
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. Recurrent Neural Network
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
11.7 RNN Short Memory
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
12.1 Basics of 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. Generative Adversarial Network (GAN)
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
14.3 Policy Gradient Method
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
