Category: AI Foundations
This category contains 19 pages.
- A Brief History of AI
- Context Windows Explained
- Diffusion Models Explained
- Fine-tuning vs RAG vs Prompting
- How Image Recognition Works (CNNs)
- How Neural Networks Learn
- How Transformers Work
- Mixture of Experts (MoE)
- Multimodal Models
- Prompt Engineering Patterns
- Reasoning Models and Test-Time Compute
- Reinforcement Learning from Human Feedback (RLHF)
- Temperature and Sampling Explained
- The Attention Mechanism
- Tokenization Explained
- Training vs Inference
- What Are Embeddings
- What Is Machine Learning
- Why LLMs Hallucinate