How AI Works
Master the fundamentals of Artificial Intelligence through structured learning paths. From basic concepts to advanced implementations - everything you need to understand AI.
Course Chapters
Chapter 1: Introduction to AI
Understanding the basics of Artificial Intelligence and its applications
Chapter 2: Machine Learning Fundamentals
Core concepts of machine learning, neural networks, and model evaluation
Chapter 3: Introduction to Language Models
Understanding language models, transformers, training, and emergent abilities
Chapter 4: Larger Context Windows
Understanding context limitations, efficient attention mechanisms, and applications of extended context
Chapter 5: Calculating Word Probabilities
Master the math behind language models with Fireship-style explanations - from basics to building your own text generator
Chapter 6: Under the Hood - Technical Foundations
Master the technical building blocks of AI: tokenization, preprocessing, loss functions, temperature, and model optimization
Chapter 7: Deep Learning Fundamentals
Master the core concepts of deep networks, training processes, and gradient descent optimization
Chapter 8: Neural Network Architecture
Deep dive into neural networks, data transformation, and multi-layer composition for building intelligent systems
Chapter 9: Image Models & Computer Vision
Complete exploration of computer vision from digit recognition to semantic geometry, covering pattern recognition, compression, and face analysis
Chapter 10: Language Analysis with Python
Master the fundamentals of text processing, from converting text to data through statistical analysis of language patterns
Chapter 11: Text Generation with Python
Master text generation through bigram analysis, frequency patterns, and complete language model implementation
Chapter 12: Build Your Own Language Model
Step-by-step guide to building a complete, production-ready language model from scratch
Select a Chapter
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💡 Tip: Complete chapters in order to unlock advanced concepts and build a solid foundation.