Foundations of
Structured Learning Paths
Curated curricula from foundational concepts to advanced research topics.
Foundations
Core concepts: What is intelligence? How do machines learn? Historical context and key paradigms.
- What is AI?
- History of AI
- Neural Networks
- Machine Learning
Architectures
Modern neural architectures: attention mechanisms, transformers, convolutional and recurrent networks.
- Transformers
- LLMs
- CNNs
- Training Dynamics
Applications
Applied techniques: prompting strategies, model adaptation, retrieval-augmented generation, agent systems.
- Prompt Engineering
- Fine-Tuning
- RAG
- AI Agents
Interactive Visualizations
Explore algorithms and architectures through hands-on demonstrations.
Neural Network Visualizer
Watch data flow through neural networks in real-time. Adjust weights, see activations.
Attention Mechanism
Explore how transformers pay attention to different words. The core of modern LLMs.
Gradient Descent
Watch optimization happen live. See how neural networks find their way downhill.
Knowledge Graph
Explore connections between AI concepts in an interactive 3D knowledge map.
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