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<h1>Learn AI by Rajul Babel - Visual Interactive Guide to AI Internals</h1>
<p><strong>Learn AI</strong> is a free interactive guide by <strong>Rajul Babel</strong>
that teaches Artificial Intelligence from first principles to production systems. Every
concept is shown with real numbers, real formulas, and progressive visual diagrams - no
watered-down explanations.</p>
<h2>What you will learn</h2>
<ul>
<li>Neural Networks - mechanics, neurons, layers, weights, activation, the forward pass.</li>
<li>Learning & Backprop - loss functions, gradients, gradient descent, deep backprop.</li>
<li>Linear Algebra for Deep Learning - vectors, dot products, matrices, activation.</li>
<li>Training Deep Networks - dropout, Adam, learning-rate schedules, initialization.</li>
<li>How LLMs Actually Train - tokenization, cross-entropy, SFT, RLHF, DPO, BPE.</li>
<li>Scaling & Modern Techniques - scaling laws, batching, distillation, CLIP.</li>
<li>The Road to Transformers - CNN, RNN, the Transformer arrives.</li>
<li>Transformer Input Pipeline - embeddings, positional encoding, RoPE.</li>
<li>Attention - Q/K/V, dot products, classroom and Google-search analogies.</li>
<li>Computing Attention - scores, K transpose, softmax, scaling, weighted sums.</li>
<li>Multi-Head Attention - the split, per-head computation, concat, W_O.</li>
<li>The Encoder - Add & Norm, FFN, residual highway, pre-norm vs post-norm.</li>
<li>The Decoder - causal masking, cross-attention, output head.</li>
<li>Modern LLM Techniques - KV cache, grouped-query attention, MoE, reasoning models.</li>
<li>Vector Search - brute-force kNN, distance metrics, IVF, HNSW, DiskANN.</li>
<li>Vector Compression - scalar / product / binary quantization, Matryoshka, IVF-PQ.</li>
<li>Vector DBs in Production - filtering, sharding, replication, hybrid search, rerankers.</li>
<li>Picking a Vector Database - FAISS, pgvector, Qdrant, Pinecone, Weaviate, Milvus, Chroma.</li>
<li>The RAG Pipeline - why retrieval, naive RAG, where it breaks.</li>
<li>RAG Data Prep - parsing, dedup, chunking strategies, contextual retrieval.</li>
<li>RAG Retrieval - embedding models, hybrid retrieval, rerankers, HyDE, query routing.</li>
<li>RAG Generation - context packing, lost-in-the-middle, Self-RAG, CRAG, GraphRAG.</li>
<li>RAG in Production - eval triangle, LLM-as-judge, RAGAS, caching, observability.</li>
<li>Prompting LLMs - system prompts, few-shot, chain-of-thought, context engineering.</li>
<li>Tools & Protocols - tool use, MCP architecture, MCP security, A2A protocol.</li>
<li>Agent Mechanics - agent loop, ReAct, plan-execute, memory taxonomy.</li>
<li>Multi-Agent Systems - orchestrator-worker, supervisor, hand-offs, critic / debate.</li>
<li>Shipping Agents - eval, guardrails, prompt-injection defense, LangGraph, CrewAI.</li>
</ul>
<h2>About the author</h2>
<p><strong>Rajul Babel</strong> built Learn AI as a free, open educational resource.
Find more at <a href="https://github.com/rajulbabel">github.com/rajulbabel</a> and
<a href="https://www.linkedin.com/in/rajulbabel">linkedin.com/in/rajulbabel</a>.</p>
<p class="loading">Loading interactive app...</p>
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<h1>Learn AI by Rajul Babel</h1>
<p>This site is an interactive guide built with React. To use the full app, please enable
JavaScript. A static summary is below.</p>
<p><strong>Learn AI</strong> is a free interactive guide by <strong>Rajul Babel</strong>
covering Neural Networks, Transformers, Attention, RAG, Vector Databases, and Agent Frameworks
- taught with real numbers, real formulas, and progressive diagrams.</p>
<p>Source: <a href="https://github.com/rajulbabel/learn-ai">github.com/rajulbabel/learn-ai</a></p>
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