A deep understanding of AI large language model mechanisms

About this course

Deep Understanding of Large Language Models (LLMs): Architecture, Training, and MechanismsDescriptionLarge Language Models (LLMs) like ChatGPT, GPT-4, , GPT5, Claude, Gemini, and LLaMA are transforming artificial intelligence, natural language processing (NLP), and machine learning. But most courses only teach you how to use LLMs. This 90+ hour intensive course teaches you how they actually work — and how to dissect them using machine-learning and mechanistic interpretability methods.This is a deep, end-to-end exploration of transformer architectures, self-attention mechanisms, embeddings layers, training pipelines, and inference strategies — with hands-on Python and PyTorch code at every step.Whether your goal is to build your own transformer from scratch, fine-tune existing models, or understand the mathematics and engineering behind state-of-the-art generative AI, this course will give you the foundation and tools you need.What You’ll LearnThe complete architecture of LLMs — tokenization, embeddings, encoders, decoders, attention heads, feedforward networks, and layer normalizationMathematics of attention mechanisms — dot-product attention, multi-head attention, positional encoding, causal masking, probabilistic token selectionTraining LLMs — optimization (Adam, AdamW), loss functions, gradient accumulation, batch processing, learning-rate schedulers, regularization (L1, L2, decorrelation), gradient clippingFine-tuning and prompt engineering for downstream NLP tasks, system-tuningEvaluation metrics — perplexity, accuracy, and benchmark dat

Course objectives

• equip students with knowledge on LLM architecture and training
• provide hands-on experience with Python and PyTorch
• enable students to create and adjust models for various applications