Section 01
Basics of Agents
RAG, agent architectures and workflows, memory and context management, frameworks, evaluation, and the MCP & A2A protocols.
Section 02GenAI and LLMs Summary
Pretraining, SFT & RLHF, policy optimization (PPO/GRPO/DPO), PEFT & quantization, transformers, attention, embeddings, sampling, and responsible AI.
Section 03Deep Learning and Machine Learning
Learning paradigms, backpropagation, losses and regularization, optimizers, classic ML algorithms, metrics, dimensionality reduction, and ML at scale.