Deep Learning
Fundamentals
The complete beginner path. Learn how neural networks actually work, from a single neuron to a full network that classifies images. No prior experience needed.
What you'll learn
By the end of this path, you'll understand how neural networks work from the ground up, be able to build and train your own models in PyTorch, and have the vocabulary to read research papers and follow ML news intelligently.
Module 1: The Basics
What is a Neural Network?
The biological inspiration, the mathematical abstraction, and why neural networks are so powerful.
Perceptrons: The Building Block
The single neuron model — weights, biases, and the dot product. Build one from scratch in Python.
Activation Functions Explained
Sigmoid, ReLU, tanh, and friends. Why non-linearity is the secret sauce that makes deep learning work.
Loss Functions: How Do We Measure Error?
MSE, Cross-Entropy, and why choosing the right loss function matters for what you're trying to solve.
Module 2: Training
Gradient Descent Visualized
The optimization algorithm at the heart of all deep learning. Watch gradients flow in an interactive demo.
Backpropagation from Scratch
The chain rule made simple. Implement backprop manually — you'll never fear it again.
The Learning Rate: Most Important Hyperparameter
Why too high breaks training, too low wastes time, and how to find the sweet spot.
Mini-Batch SGD & Optimizers
From vanilla SGD to Adam. Why modern optimizers train faster and more reliably.
Module 3: Building Real Networks
Multi-Layer Perceptrons (MLPs)
Stack layers, add depth, gain expressive power. Architecture design principles for beginners.
Overfitting & Regularization
Why models that look great in training fail in the real world — and how to fix it with dropout, L2, early stopping.
Batch Normalization
The surprisingly powerful normalization trick that stabilizes training and allows deeper networks.
🏆 Your First Neural Network in PyTorch
Put it all together. Build, train, and evaluate an MNIST digit classifier. Full working code included.
Ready to go deeper?