AI & Machine Learning
How AI systems work under the hood — from neural networks and transformers to RAG, agent patterns, and responsible deployment.
Foundations
Neural Networks Basics
Neurons, layers, activation functions, forward & backward propagation.
Embeddings & Vector Databases
Text → vectors, similarity search, Pinecone, Qdrant, and pgvector.
Transformer Architecture
Self-attention, positional encoding, and why LLMs work so well.
Tokenization & Context Windows
BPE, SentencePiece, token limits, and context management strategies.
Patterns & Training
Deployment & Ethics