Interactive AI Book

Learn AI concepts through interactive visualizations and progressive exploration

📚 Table of Contents

Chapter 1: Fundamentals of AI & Machine Learning
  • Data Scaling
  • Linear Regression
  • Loss and Metric
  • Gradient Descent
  • Stochastic Gradient Descent
  • Tensors
Chapter 2: Neural Networks
  • Perceptron 🔒
  • Backpropagation 🔒
  • Multi-layer Neural Networks 🔒
Chapter 3: Deep Learning Essentials
  • Introduction to Deep Learning 🔒
  • Data for Training 🔒
  • Activation Functions 🔒
  • Optimizers 🔒
  • Loss Functions 🔒
  • Visual Demos 🔒
  • Manage Model 🔒
Chapter 4: Computer Vision
  • Visual Demo 🔒
  • Image Data 🔒
  • Images to Training Input 🔒
  • Convolution Kernels 🔒
  • Pooling 🔒
Chapter 5: Audio Processing
  • Visual Demo 🔒
  • Audio Basics 🔒
  • Speech Sampling 🔒
Chapter 6: Natural Language Processing
  • Spam Detection 🔒
  • Question and Answer 🔒
  • Word Embeddings 🔒
  • Position Embeddings 🔒
  • Attention Score 🔒
  • Attention Heatmap 🔒

Chapter 1: Fundamentals of AI & Machine Learning

Build a solid foundation in the basics of machine learning, from data preparation to optimization techniques.

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Loading Tensors...

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Chapter 2: Neural Networks

Discover how neural networks learn through perceptrons, backpropagation, and multi-layer architectures.

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Chapter 3: Deep Learning Essentials

Master the core concepts of deep learning including activation functions, optimizers, and model management.

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Chapter 4: Computer Vision

Learn how to process and analyze visual data using convolutional neural networks and image processing techniques.

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Chapter 5: Audio Processing

Explore audio data processing, from basic signal processing to speech sampling techniques.

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Chapter 6: Natural Language Processing

Dive into text processing with word embeddings, attention mechanisms, and transformer models like BERT.

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