AI & ML Engineering Live

AI & Machine Learning Engineering Mastercourse is a comprehensive, production-grade curriculum designed for software engineers, systems architects, and machine learning practitioners. Built strictly from first principles, this curriculum takes you from core mathematical notation to distributed neural network training and autonomous LLM agents in production. ========================================== WHAT YOU WILL LEARN (16-BOOK CURRICULUM) ========================================== • PART 1: MATHEMATICAL & SYSTEMS FOUNDATIONS - Vector spaces, matrix decompositions, eigenvalues, and SVD - Multivariable calculus, gradient vectors, Jacobians, and Hessians - Convex optimization, stochastic gradient descent (SGD), AdamW - Probability distributions, information theory, and cross-entropy loss • PART 2: DEEP LEARNING FROM FIRST PRINCIPLES - Autograd engines from scratch (computational graph and backpropagation) - Multi-Layer Perceptrons (MLP), forward-backward passes, activation dynamics - Convolutional Neural Networks (CNNs) and Vision Transformers (ViT) - Recurrent neural dynamics, LSTMs, and sequence transduction • PART 3: MODERN TRANSFORMER ARCHITECTURES - Self-attention, Scaled Dot-Product, and Multi-Head Attention (MHA) - Modern rotary positional embeddings (RoPE) and attention masking - Key-Value Cache (KV-Cache) optimization and latency reduction - Decoder-only vs. encoder-decoder architectures • PART 4: DISTRIBUTED TRAINING & SYSTEMS ENGINEERING - Data Parallelism (DDP) and gradient synchronization primitives - Tensor Parallelism (TP) and Pipeline Parallelism (PP) - Fully Sharded Data Parallel (FSDP) and ZeRO memory optimization - Mixed-precision training (FP16, BF16, FP8) and GPU memory footprinting • PART 5: PRODUCTION LLM SYSTEMS & AUTONOMOUS AGENTS - Pre-training, instruction fine-tuning, and PEFT (LoRA, QLoRA) - Retrieval-Augmented Generation (RAG): chunking, dense embeddings, vector search - Function calling, autonomous agent loops, and tool orchestration - LLM inference serving, continuous batching, and vLLM architecture ========================================== 8 FLAGSHIP PRODUCTION PROJECTS ========================================== 1. MicroGrad Autograd Engine: Pure Python computational graph with backward pass 2. Neural Language Model: Character and BPE tokenized generative transformer 3. High-Performance CNN Classifier: Optimized PyTorch computer vision pipeline 4. Transformer from Scratch: Complete multi-head decoder model implementation 5. Distributed PyTorch Pipeline: Multi-GPU data-parallel training workflow 6. Enterprise RAG Pipeline: Vector retrieval, hybrid re-ranking, context synthesis 7. Autonomous AI Agent: Multi-step reasoning with API execution and tool routing 8. Quantized LLM Inference Engine: Low-latency serving and memory optimization ========================================== ENGINEERING COMPANION MANUALS ========================================== • Mathematical Notation & Formulation Guide • Architecture Blueprint & Design Catalog • Systems & Scaling Production Reference • Engineering Debugging Playbook • Production Incident Post-Mortem Analysis ========================================== APPLICATION FEATURES ========================================== • 81 In-Depth Modules: Complete coverage across 16 master books • Zero Ads: 100% focused, distraction-free reading experience • Dynamic Reading Themes: Obsidian Dark, Editorial Paper White, and Warm Sepia • Content Protection: Anti-capture protection for curriculum integrity • Offline Native Performance: Ultra-fast local SQLite database with zero tracking Master artificial intelligence and machine learning engineering from first principles to enterprise deployment.