Chapter 13: Machine Learning & Deep Learning Foundations

The learning algorithms: supervised and unsupervised learning, neural-network mechanics, domain-specific deep-learning architectures, and Transformers.

• Supervised Learning: Linear Models, SVMs, k-NN, Decision Trees, and Gradient Boosting (XGBoost, LightGBM, CatBoost)
• Unsupervised Learning: Clustering, Principal Component Analysis, and Autoencoders
• Neural Network Mechanics: Forward/Backpropagation, Activation Functions, Loss Functions, and Optimizers (Adam, SGD)
• Deep Learning Architectures: CNNs, RNNs, LSTMs, GRUs, 3D CNNs, SlowFast, Video Transformers, YOLO, ByteTRACK, and DeepSORT
• Transformer Architecture: Self-Attention Mechanics, Scaled Dot-Product, Positional Encodings, Multi-Head Attention, and FlashAttention Mechanics
• Chapter 13 References