| 週次 |
授課內容 |
| 第1週 |
Course Logits
Regression ( I II )
|
| 第2週 |
Multi-Layer Perceptron
Non-Linearity of MLP
Implementation: Retrieving Data
Forward Propagation and Cost (*)
Back Propagation (*) |
| 第3週 |
Cost and Objective
Against Overfitting: Regularization and Perturbations
Numerical Stability and Initialization
Implementation: MLP |
| 第4週 |
TensorFlow: Brief Intro
Building Computation Graph: Node as Tensorflow Layer
Information Storage and Transmission among Tensorflow Layers |
| 第5週 |
Sequential Model
Activation Function
Parameter Assessment
From Python function to Tensorflow Graph |
| 第6週 |
Storing Model and Visualization
Gradient Tape ( I II )
Native Training Loop in Tensorflow
Training and Inference Using tf.keras.Model |
| 第7週 |
Model.Compile()
Model.Fit()
Customize Fit()
Customize Callbacks |
| 第8週 |
Sequencing and Preprocessing in Tensorflow
Dataset in Tensorflow
TFRecord data format |
| 第9週 |
The Representation of Data and Signal
Independent Component Analysis
MAP Assumption
Morphology Description |
| 第10週 |
Mutual Coherence
Dictionary Learning
Convolutional Dictionary Learning
ADMM for CDL |
| 第11週 |
Joint ADMM with Convergence Property
Adaptive ADMM for CDL
Fundamental Elements of Convolution Net |
| 第12週 |
Convolution Layer in TensorFlow
Family of LeNet
Implementation: LeNet, AlexNet, VGG
Batch Normalization |
| 第13週 |
ResNet
DenseNet
Introduction to Recurrent Neural Net
Sequential Data
Dealing Numeric Sequential Data in TensorFlow |
| 第14週 |
Attention Mechanism |
| 第15週 |
Optimization Methods |
| 第16週 |
Generative Adversarial Network
|
| 第17週 |
Reinforcement LearningApplications
|
| 第18週 |
Final project presentation |