Part 1 Introduction to AI
1. Introduction
1. Artificial Intelligence2. History of Neural Networks
3. Characteristics of Deep Learning
4. Applications of Deep Learning
5. Deep Learning Frameworks6. 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 Recognition3.9 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 Tensor4.5 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 PracticePart 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 Layer6.6 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 Functions7.4 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. Overfitting9.1 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 Network10.1 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 Layer10.8 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
