PATH 01
Foundations
Rules, search, constraints, and planning—the ideas that make intelligent problem-solving explicit.
→Tutorial index
Tutorials
Explore practical tutorials that connect the underlying theory to a small, observable problem and a credible real-world application.
Learning paths
Understand AI. Build with it. Ship it safely.
Start with the ideas behind intelligent systems, then move through practical agent building and the engineering discipline required to put an AI-built application into production.
Understand AI
PATH 02
Core Machine Learning
Prediction, classification, clustering, dimensionality reduction, and ensemble methods grounded in data.
→PATH 03
Deep Learning
Perceptrons, convolution, sequence models, attention, and transfer learning through working examples.
→PATH 04
Advanced & Generative AI
A planned systems path for embeddings, retrieval, evaluation, tools, agents, guardrails, and deployment.
→Build and ship
PATH 05
Build With AI Agents
Move from a clear idea to a working prototype with repository instructions, coding agents, and tested implementation loops.
→PATH 06
Ship to Production
Make an AI-built application reliable with security checks, tests, observability, and the operational judgment production requires.
→All published tutorials
Shipping an AI System: Architecture, Observability, Cost, and Reliability
Package an evaluated AI workflow behind a local API boundary with structured logs, latency accounting, fallbacks, privacy controls, and rollback thinking.
Securing AI Applications: Prompt Injection, Data Leakage, and Tool Safety
Build a harmless local prompt-injection test lab and map trust boundaries, least privilege, output validation, sandboxing, approvals, and incident response.
Evaluating Generative AI: From Vague Impressions to Reproducible Tests
Create a small golden dataset and evaluation harness for groundedness, citations, retrieval, task success, regression, and uncertainty.
Agent Memory: What AI Systems Should Remember—and What They Should Forget
Implement short-term state, episodic records, and retrieval-based memory while handling retention, stale facts, conflicts, privacy, and deletion.
AI Agents: Models, Tools, State, and the Execution Loop
Build a bounded tool-using agent with explicit state, permissions, retries, and stopping rules, then place it inside an operational investigation workflow.
Beyond Basic RAG: Hybrid Search, Reranking, and Better Retrieval
Compare lexical, dense, hybrid, and reranked retrieval on a small labelled set, then decide when the extra complexity is justified by measurable search failures.
Retrieval-Augmented Generation: Giving Language Models Verifiable Context
Build a small citation-aware retriever, inspect ranked passages before generation, and learn why chunking, permissions, freshness, and evaluation matter more than adding context blindly.
Tool-Using AI: From Free-Form Text to Reliable Actions
Build a provider-agnostic tool dispatcher that validates arguments, enforces an allow-list, executes deterministic functions, and handles retries without treating generated text as permission.
Context Engineering: Designing the Information an AI System Actually Sees
Treat instructions, examples, retrieved evidence, tools, constraints, and output schemas as one context system, then test a local structured-extraction pipeline under a fixed budget.
Fine-Tuning Language Models: Full Training, LoRA, and the Practical Middle Ground
Understand what full fine-tuning changes, how adapters and LoRA reduce trainable parameters, and how to choose adaptation only after a baseline and held-out evaluation.
How Language Models Generate Text: Temperature, Top-k, and Top-p
Inspect a language model’s next-token distribution, then measure how temperature, top-k, top-p, and min-p change uncertainty and sampled support-ticket continuations.
Next-Token Prediction: The Learning Objective Behind Language Models
Train a tiny causal language model from scratch, inspect shifted labels, compare perplexity with a uniform baseline, and see overfitting appear on a small…