Neural Networks & Machine Learning

ML (machine learning) enables systems to learn patterns from data without explicit programming. This pack covers the core theory and practical knowledge for building, training, and deploying NN (neural network) based systems.

Learning Paradigms

SL (supervised learning): model learns mapping from input X to output Y given labeled training pairs (x_i, y_i). REG (regression) predicts continuous values; CLF (classification) predicts discrete categories. The goal: minimize expected loss over unseen data (generalization), not just training data (memorization).

UL (unsupervised learning): discovers structure in unlabeled data. Clustering (k-means, DBSCAN, hierarchical), dimensionality reduction (PCA, t-SNE, UMAP), density estimation, and generative models. Autoencoders learn compressed representations by encoding input to latent space then reconstructing.

RL (reinforcement learning): agent learns policy (state-to-action mapping) by interacting with environment and receiving rewards. Key challenge: exploration vs exploitation tradeoff. Q-learning learns action-value function; policy gradient methods directly optimize the policy. PPO (Proximal Policy Optimization) is the current workhorse for continuous control and RLHF.

SSL (self-supervised learning) has emerged as the dominant pre-training paradigm. Masked language modeling (BERT), autoregressive prediction (GPT), contrastive learning (SimCLR, CLIP) — all create supervision signals from the data itself, eliminating the labeling bottleneck.

Neural Network Fundamentals