Understand how artificial neurons, layers, and activation functions combine to power every Large Language Model. Interactive visualization of a mini neural network with forward pass, backpropagation, and activation functions.

Understand how LLMs are trained: loss functions, gradient descent, learning rate schedules, and the Adam optimizer. Interactive visualization of the loss landscape, optimizer comparison, and LLM-scale training.

Understand transfer learning — the paradigm shift that made modern LLMs possible. Explore pretraining, fine-tuning, and in-context learning with interactive visualization of the transfer learning pipeline.

Learn how Large Language Models tokenize text using Byte-Pair Encoding. Interactive visualization shows real-time tokenization from GPT and Claude.

Learn how embeddings represent words as vectors in high-dimensional space. See how similar words cluster together in interactive 3D visualization.

Understand how attention mechanism helps Transformers focus on relevant information. Interactive visualization of attention weights in real-time.

Understand the Transformer architecture powering GPT, Claude, and Llama. Interactive visualization of encoder-decoder structure with attention layers.

Understand prefill vs generation phases, KV-Cache optimization, and why the first token is slow. Learn how to optimize inference latency and throughput.

Master Greedy, Beam Search, Temperature, Top-k, and Top-p sampling. Interactive demos show how each parameter affects LLM output creativity and coherence.

Learn how to control LLM behavior with generation parameters. Understand Temperature, Top-p, Frequency Penalty, and Presence Penalty with live examples.

Understand the key components that make up effective prompts

Master core principles of prompt engineering. Learn clear instructions, context setting, output formatting, and role assignment for better LLM responses.

Master proven techniques for writing high-quality prompts

Reduce model size with FP16, INT8, INT4 while preserving quality

Learn when to use prompting, LoRA, or full fine-tuning for your use case