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Machine learning basics. This part briefly introduces the fundamental ML problems-- regression, classification, dimensionality reduction, and clustering-- and the traditional ML models and numerical algorithms for solving the problems.
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Classification.
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Logistic regression: [slides] [lecture note]
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SVM: [slides]
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Softmax classifier: [slides]
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KNN classifier: [slides]
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Clustering. [slides]
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Dimensionality reduction. [slides-1] [slides-2] [lecture note]
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Scientific computing libraries. [slides]
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Monte Carlo. [slides] [Video (in Chinese)]
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Neural network basics. This part covers the multilayer perceptron, backpropagation, and deep learning libraries, with focus on Keras.
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Multilayer perceptron and backpropagation. [slides] [lecture note]
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Keras. [slides]
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Further reading:
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Convolutional neural networks (CNNs). This part is focused on CNNs and its application to computer vision problems.
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CNN basics. [slides]
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Tricks for improving test accuracy. [slides]
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Feature scaling and batch normalization. [slides]
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Advanced topics on CNNs. [slides]
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Popular CNN architectures. [slides]
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Face recognition. [slides]
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Further reading:
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[style transfer (Section 8.1, Chollet's book)]
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[visualize CNN (Section 5.4, Chollet's book)]
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Recurrent neural networks (RNNs). This part introduces RNNs and its applications in natural language processing (NLP).
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Categorical feature processing. [slides] [Video (in Chinese)]
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Text processing and word embedding. [slides] [Video (in Chinese)]
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RNN basics. [slides] [Video (in Chinese)]
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LSTM. [slides] [reference] [Video (in Chinese)]
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Making RNNs more effective. [slides] [Video (in Chinese)]
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Text generation. [slides] [Video (in Chinese)]
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Machine translation. [slides] [Video (in Chinese)]
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Attention. [slides] [Video (in Chinese)] [reference-1] [reference-2]
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Self-Attention. [slides] [Video (in Chinese)]
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Language Models beyond RNNs.
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Transformer (1/2): attention without RNN. [slides] [Video (in Chinese)]
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Transformer (2/2): from shallow to deep. [slides] [Video (in Chinese)] [reference]
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BERT: pre-training Transformer. [slides] [Video (in Chinese)] [reference]
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Autoencoders. This part introduces autoencoders for dimensionality reduction and image generation.
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Generative Adversarial Networks (GANs).
- DC-GAN [slides]
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Deep Reinforcement Learning.
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Reinforcement learning [slides] [lecture note] [Video (in Chinese)]
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Value-based learning [slides] [Video (in Chinese)]
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Policy-based learning [slides] [Video (in Chinese)]
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Actor-critic methods [slides] [Video (in Chinese)]
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AlphaGo [slides] [Video (in Chinese)]
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Parallel Computing.
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Basics and MapReduce. [slides] [lecture note] [Video (in Chinese)]
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Parameter Server and Decentralized Network. [slides] [Video (in Chinese)]
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TensorFlow's Mirrored Strategy and Ring All-Reduce. [slides] [Video (in Chinese)]
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Federated Learning. [slides] [Video (in Chinese)]
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Adversarial Robustness. This part introduces how to attack neural networks using adversarial examples and how to defend from the attack.
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Data evasion attack and defense. [slides] [lecture note]
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Data poisoning attack. [slides] [Video (in Chinese)]
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Further reading: [Adversarial Robustness - Theory and Practice]
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Meta Learning.
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Few-shot learning: basic concepts. [slides] [Video (in Chinese)]
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Siamese network. [slides] [Video (in Chinese)]
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Pretraining + fine tuning. [slides] [Video (in Chinese)]
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Recommender System. This part is focused on the collaborative filtering approach to recommendation based on the user-item rating data. This part covers matrix completion methods and neural network approaches.
- Collaborative filtering. [slides]