AI & Machine Learning Learning Hub
Learn artificial intelligence by seeing and experimenting with it. Step inside neural networks, adjust attention weights, inspect training gradients, and build miniature language models in your browser.
Core Domains & Tracks
Zero Login • 100% FreeLarge Language Models (LLMs)
Master autoregression, subword tokenization, high-dimensional embeddings, self-attention, and sampling.
Retrieval-Augmented Generation (RAG)
Connect models to enterprise facts: document chunking, vector embeddings, similarity search, and reranking.
Classical Machine Learning
Supervised and unsupervised foundations: Gradient Descent, K-Means clustering, Linear Regression, and Evaluation.
Deep Learning & Neural Networks
Forward propagation, Cross-Entropy Loss, backpropagation calculus chain rule, and AdamW optimization.
Fine-Tuning, LoRA & PEFT
Adapt foundation models to specialized tasks using low-rank decomposition adapters and JSONL datasets.
AI & ML Technical Interview Arena
Prepare for Machine Learning and AI Engineer interviews with questions, answers, and spaced repetition.
Flagship Educational Articles
How an LLM Generates an Answer: From Text to the Next Token
Trace the computational journey of a user prompt through tokenization, attention, and probability distributions.
Math & LossHow Language Models Learn: Training Explained for Beginners
Trillions of tokens, forward passes, cross-entropy loss, backpropagation calculus, and AdamW weight updates.
Hands-on CodeBuild a Tiny Language Model to Understand How LLMs Learn
Construct an educational next-token predictor in your browser to inspect vocabulary, training loss, and generation.