
Course Requirements 
 Kaggle homework (40%)
 Midterm & Final exam (20% & 15%)
 Final project (20%)
 Quizzes (5%)
 Bonus (5%)

pdf




Text Book 
François Chollet, Deep Learning with Python, 2nd Edition, Manning, 2021


GitHub 


Reference 
Ian Goodfellow and Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016


slides 

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Past, Present, and Future of AI 
 Free your imagination to unleash your potential!




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

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2 
Applied Math 
Linear Algebra
 Scalar, vector, matrix, tensor
 Matrix multiplication
 Dot product and outer product
 Lp norm

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Probability
 Mutually exclusive & independence
 Conditional Probability & Bayes Theorem
 Naive Bayes classifier

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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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IMDB_review
financial_news
house_pricing


4 
Machine Learning Basics 
 Five tribes of Machine Learning
 KNN, Perceptron, SVM
 Logistic Regression
 Linear, Ridge, Lasso Regression and ElasticNet
 Regularization
 Overfitting, Crossvalidation, Dropout
 Confusion Matrix, Accuracy, Precision, Recall

pdf 
classifiers
regressors


5 
Convolutional Neural Networks (CNNs) 
 Convolution Filters
 Kernel vs. Filter
 Using pretrained models
 AlexNet, Inception, ResNet, Xception
 NAS & EfficientNet

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6 
Advanced Keras API 
 Functional API
 Multiinput and Multioutput model
 Depthwise Separable Convolution

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7 
NLP & Word Embedding 
 Natural Language Processing
 Ngram & Skipgram Model
 Word2vec
 GloVe

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8 
RNN & LSTM 
 Recurrent Neural Networks
 LSTM
 Temporal CNN

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9 
Attention & Transformer 
 Attention is All You Need
 Selfattention
 Transformer

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Modern NLP Models 
 Bidirectional Encoder Representations from Transformers (BERT)
 Generative Pretrained Transformers (GPT)
 Embeddings from Language Models (ELMo)

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10 
Generative Deep Learning 
 Generating Text using LSTM
 Neural Style Transfer
 Variational Auto Encoder

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Generative Adversarial Networks (GANs) 
 Generative Adversarial Networks
 Conditional GAN
 CycleGAN
 GauGAN, Adversarial Attack

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11 
Object Detection and Segmentation 
 Region Proposal & RCNN
 YOLO v1  v5
 SSD, FPN, Retina Net, EfficientDet
 UNet, Mask RCNN

pdf 
Run YOLOv4


12 
Deep Reinforcement Learning 
 What is Reinforcement Learning?
 State, Action, Reward and Policy
 Valuebased vs. Policybased Learning
 Onpolicy vs. Offpolicy

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13 
Action Recognition 
 Optical Flow
 Twostream Model
 C3D, TSN, and other SOTA Models

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14 
Deep Learning on Graphs 
 Graphs and Networks
 Vertices, Edges, Degree
 Deep Random Walk
 Graph Convolutions

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Final Project Demo 



