AI Fundamentals
Beginner to Intermediate • 14 lessons • 3-4 hours total
Build a comprehensive foundation in artificial intelligence. From basic neural networks to advanced architectures, learn the essential concepts that power modern AI through interactive visualizations.
What You'll Learn
Neural Network Basics
How neurons, layers, weights, and biases work together to process information.
Learning & Optimization
Gradient descent, backpropagation, and how networks improve through training.
Activation & Loss Functions
Why non-linear functions and proper loss measurement are crucial for learning.
Generalization & Regularization
Preventing overfitting and ensuring models work on new, unseen data.
Specialized Architectures
CNNs for vision, RNNs for sequences, and embeddings for representation learning.
Practical ML Skills
Data preprocessing, hyperparameter tuning, and ensemble methods for real-world applications.
Interactive Visualizations
Perceptron & Linear Classification
Discover the foundation of all neural networks! Watch a perceptron learn to predict weather by drawing a line that separates rainy days from sunny days.
Data Preprocessing & Feature Engineering
Learn why 'garbage in, garbage out' matters! Discover how data normalization and feature scaling dramatically improve learning.
Activation Functions Deep Dive
Discover why neural networks need non-linear functions! Compare ReLU, Sigmoid, and Tanh through interactive examples and see how they shape learning.
Neural Network Forward Pass
Follow Alex's story as a trained network predicts test performance. See how data flows through layers and predictions are made.
Loss Functions & Metrics
Learn how neural networks measure 'wrongness'! Explore different loss functions and understand when to use each for classification vs regression.
Gradient Descent Optimization
Explore how neural networks learn by optimizing their parameters. See gradient descent navigate loss landscapes to find optimal solutions.
Backpropagation Step-by-Step
Uncover the magic behind neural network learning! Watch gradients flow backward through layers and see how each weight gets updated.
Neural Network Training
Watch a neural network learn from scratch! See how random weights become intelligent through training on real student data.
Overfitting & Regularization
Understand the balance between learning and memorizing! See how dropout and regularization help models generalize to new data.
Hyperparameter Tuning
Master the art of model configuration! Learn how to find the best learning rates, layer sizes, and other settings for optimal performance.
Convolutional Neural Networks (CNNs)
Discover how computers see! Watch convolutional filters detect edges, shapes, and patterns in images through sliding window operations.
Recurrent Neural Networks (RNNs)
Learn how neural networks remember! Watch RNNs process sequences by maintaining hidden states and see why they struggle with long sequences.
Embeddings & Representation Learning
See how words become numbers with meaning! Explore how neural networks learn to represent concepts as vectors in high-dimensional space.
Ensemble Methods
Discover the power of teamwork in AI! See how combining multiple models through bagging and boosting creates stronger predictions.
Next Steps
After completing AI Fundamentals, continue your learning journey