Personalized journeys from absolute beginner to AI researcher
New to AI? Start from the absolute basics - computers, code, and intelligence.
Prefer diagrams and interactive demos? Explore AI through visualizations.
Build AI applications. Focus on practical implementation and code.
Deep dive into theory, proofs, and cutting-edge research. Graduate-level rigor.
Just the essentials. Get up to speed in under 2 hours.
Core concepts everyone should know.
A concise introduction to Artificial Intelligence — core ideas and where it shows up.
Practical strategies for understanding and working with AI in everyday contexts.
Practical strategies for using AI tools effectively in your daily work and creative projects.
Exploring the nature of intelligence, both human and artificial.
From symbolic systems to deep learning and beyond — a brief history.
Machine learning and deep learning fundamentals.
Key concepts: datasets, features, training/validation, bias-variance, and generalization.
Understanding how neural networks learn and make decisions.
How models learn: gradients, loss functions, and optimizers.
Scaling neural networks: architectures, representation learning, and training dynamics.
Neural network architectures powering modern AI.
Convolutions, receptive fields, and modern vision architectures.
Sequential modeling: recurrence, gating, and sequence learning.
Attention mechanisms, self-attention, scaling laws, and the transformer revolution.
How models like GPT and Claude understand and generate text.
Creating content with AI: images, text, and beyond.
How diffusion-based generative models work and how to use them.
Principles and patterns for effective prompting across tasks.
Combining search and generation for grounded outputs.
Adapting models and aligning behavior with human feedback.
Mathematical foundations and advanced theory.
The mathematical foundations powering modern AI - linear algebra, calculus, and probability explained at three levels.
A comprehensive journey from silicon to superintelligence.
Agents, environments, policies, value functions, and deep RL.
Safety, ethics, and where AI is headed.
Risks, safety frameworks, and alignment strategies.
Responsible AI: fairness, accountability, transparency, and governance.
Agent architectures, planning, tool use, and evaluation.
Scenarios, economics, governance, and societal impact.
Explore the interactive knowledge graph to visualize relationships between AI concepts. Perfect for understanding the big picture.
Explore Knowledge Graph