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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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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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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 (Chinese)].
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Text processing and word embedding [slides] [video (Chinese)].
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RNN basics [slides] [video (Chinese)].
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LSTM [slides] [reference] [video (Chinese)].
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Making RNNs more effective [slides] [video (Chinese)].
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Text generation [slides] [video (Chinese)].
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Machine translation [slides] [video (Chinese)].
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Attention [slides] [video (English)] [video (Chinese)] [reference].
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Self-attention [slides] [video (English)] [video (Chinese)].
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Transformer Models.
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Transformer (1/2): attention without RNN [slides] [video (English)] [video (Chinese)].
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Transformer (2/2): from shallow to deep [slides] [video (English)] [video (Chinese)] [reference].
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BERT: pre-training Transformer [slides] [video (English)] [video (Chinese)] [reference].
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Vision Transformer (ViT) [slides] [video (English)] [video (Chinese)].
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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 basics [slides] [lecture note] [video (Chinese)].
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Value-based learning [slides] [video (Chinese)].
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Policy-based learning [slides] [video (Chinese)].
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Actor-critic methods [slides] [video (Chinese)].
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AlphaGo and Monte Carlo tree search [slides] [video (Chinese)].
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Parallel Computing.
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Basics and MapReduce [slides] [lecture note] [video (Chinese)].
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Parameter server and decentralized network [slides] [video (Chinese)].
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TensorFlow's mirrored strategy and ring all-reduce [slides] [video (Chinese)].
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Federated learning [slides] [video (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 (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 (English)] [video (Chinese)].
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Siamese network [slides] [video (English)] [video (Chinese)].
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Pretraining + fine tuning [slides] [video (English)] [video (Chinese)].
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Neural Architecture Search (NAS).
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Basics [slides] [video (Chinese)].
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RNN + Reinforcement Learning: [slides] [video (Chinese)].
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Differentiable NAS: [slides] [video (Chinese)].
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