Module 1 - Foundations revision

Revision guide · ~30 min · dense bullets, not full lessons

Plain-English bullets for chapters 1.1–1.3. Open full lessons when a bullet feels fuzzy.

1.1 AI & Deep Learning Essentials

Stack of ideas

• AI (Artificial Intelligence) — building machines that act smart enough for a task (observe -> decide -> succeed).
• ML (Machine Learning) — learn patterns from data instead of hand-coding every rule.
• Deep learning — ML with multi-layer neural nets that learn hierarchical features from raw inputs.
• GenAI (Generative AI) — models that create new content (text, images, code) by sampling from a learned distribution.

Classical vs learned

• Rules, search, and expert systems scale poorly in noisy high-dimensional worlds (vision, speech, open language).
• Supervised learning: labeled inputs plus answers. Unsupervised: find structure in unlabeled data. RL (Reinforcement Learning): learn from actions plus rewards.

Representation

• One-hot — sparse, no similarity between words. Embeddings — dense vectors; nearby = similar meaning.
• Word2Vec / GloVe intuition: "you shall know a word by the company it keeps."

Neuron to MLP

• Linear: z = w*x + b. Nonlinearity (ReLU, sigmoid, tanh) makes depth useful.
• MLP (Multilayer Perceptron) = stacked linear + activation. Universal approximator in theory; practice needs data, regularization, right capacity.
• Backpropagation = chain rule through the graph. Gradient descent: update weights by subtracting learning_rate * gradient_of_loss.
• Overfit vs underfit; bias-variance tradeoff. Fix overfit: more data, dropout, weight decay, early stop. Fix underfit: capacity / train longer / better features.

Quick self-check

• Can you place ChatGPT inside AI > ML > DL > GenAI?
• Why does ReLU help vanishing gradients vs deep sigmoids?

1.2 Deep Learning Essentials

Training loop

• Batch -> forward -> loss -> backward -> optimizer step. Epoch = one pass over data.
• Learning rate too high -> diverge; too low -> crawl. Schedules and warmups matter for big models.
• Train / val / test split. Never tune on test.

CNN (Convolutional Neural Network, vision intuition)

• Convolution shares filters spatially; translation equivariance; local receptive fields.
• Pooling / strides reduce spatial size. Depth stacks simple edges -> parts -> objects.

RNN / LSTM (Recurrent Neural Network / Long Short-Term Memory, sequence intuition)

• Hidden state carries past tokens. Vanilla RNNs struggle with long dependencies (vanishing/exploding gradients).
• LSTM/GRU gates control what to keep/forget — still sequential -> hard to parallelize -> transformers win for most NLP.

1.3 Transformer Architecture

Why transformers

• Parallel over sequence (vs RNN step-by-step). Attention = soft lookup of relevant context.
• Scales with data + compute; foundation of modern LLMs.

Self-attention (Q, K, V)

• For each position: query asks "what do I need?", keys answer "what do I contain?", values carry content.
• Scores: softmax(Q * K^T / sqrt(d_k)) * V. Divide by sqrt(d_k) so softmax stays well-behaved as dimension grows.
• Multi-head: several subspaces in parallel (syntax vs semantics, etc.).

Positional encoding

• Attention is permutation-invariant without positions. Add / inject position (sinusoidal, learned, RoPE in modern LLMs).

Decoder stack (GPT-style)

• Causal mask — position i cannot attend to later positions (autoregressive next-token training).
• Cross-attention (encoder-decoder MT models): decoder queries encoder keys/values. Decoder-only LLMs skip a separate encoder.

Autoregressive decoding

• Train: predict next token given left context. Infer: sample / greedy / beam / nucleus (top-p) / temperature.
• Higher temperature -> flatter distribution (more random). top-p keeps the smallest set of tokens whose probabilities sum to at least p.

Must-remember diagram in your head

tokens -> embed+pos -> [N x (MHA + FFN + residual + LN)] -> logits -> next token

30-minute drill

1. Explain attention to a rubber duck without jargon for 2 minutes.
2. Sketch one transformer block (residual + LN placement).
3. Name three failure modes of deep nets (overfit, vanishing grad, poor LR) and one fix each.