
Text Book 
François Chollet, Deep Learning with Python, Manning, 2017


GitHub 


1 
Introduction to Deep Learning 
 What is the Machine Learning?
 Neural Networks, Gradient Descent and Backpropagation
 Stateofthearts of deep learning

pdf 


2020/2/29 
2 
Applied Math 
 Basic concepts of Linear Algebra, Probability, Calculus and Optimization

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2020/3/7 
3 
Introduction to Keras 
 Write Keras code on Google Colab
 Create a simple Dense Neural Networks
 Use DNN to solve classification and regression problems
 Batch, Epoch and Learning rate

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2020/3/17 
4 
Machine Learning Basics 
 Five tribes of Machine Learning
 Least Squares Regression
 Logistic Regression
 Support Vector Machine
 Overfitting, Crossvalidation, Dropout
 Confusion Matrix

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2020/3/24 
5 
Convolutional Neural Networks (CNN) 
 Convolution Filters
 Kernel vs. Filter
 Max Pooling
 AlexNet, ResNet, DenseNet, Inception
 NAS and EfficientNet

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2020/3/31 
61 
Word Embedding 

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2020/4/7 
62 
RNN & LSTM 
 Recurrent Neural Networks
 LSTM
 Temporal CNN

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2020/4/7 
63 
Attention, Transfomer 
 Selfattention
 Transfomer
 BERT

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2020/4/7 
7 
Advanced Keras API 
 Functional API
 Multiinput and Multioutput model

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2020/4/7 
8 
Generative Deep Learning 
 Generating Text using LSTM
 Neural Style Transfer
 Variational Auto Encoder
 Generative Adversarial Networks
 Pix2Pix, CycloneGAN
 Adversarial Attack

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2020/4/7 
9 
Object Detection and Segmentation 
 Classification & Detection and Segmentation
 RCNN, SSD, YOLO, EfficientDet

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2020/5/5 
10 
Action Recognition 
 Action Recognition and Action Event Detection
 C3D, TSN, and other Models

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2020/5/5 
11 
Deep Reinforcement Learning 
 What is Reinforcement Learning?
 State, Action, Reward and Policy
 Valuebased vs. Policybased Learning
 Onpolicy vs. Offpolicy

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2020/6/2 
12 
Deep Learning on Graphs 
 Graphs and Networks
 Vertices, Edges, Degree
 Deep Random Walk
 Graph Convolutions

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2020/6/9 

Fina Project Demo 




2020/7/4 