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Module · 6 lessons
Deep Learning Foundations
Build neural networks from scratch and understand backpropagation and the optimizers that train them.
At a glance
Level
Advanced
Lessons
6 lessons
Time to complete
2–3 weeks
Cost
Free forever · no sign-up
Before reaching for a framework, it pays to understand what a neural network actually does. This module builds neural networks from scratch with NumPy, derives backpropagation step by step, and explains the optimizers — SGD, RMSprop, and Adam — that make training work.
Lessons in this module
1
Introduction to Neural Networks
Build intuition for how neural networks work, from the artificial neuron to layers, weights, biases, and activation functions
2
Math and NumPy Foundations for Neural Networks
Learn the linear algebra that powers neural networks and implement a neuron and a dense layer forward pass in NumPy
3
Gradient Descent for Neural Networks
Understand how neural networks learn by minimizing a loss function with gradient descent, and train a small numpy network on real data
4
Backpropagation
Understand backpropagation: how a neural network computes the gradient of every weight efficiently with the chain rule
5
Optimizers: SGD, RMSprop, and Adam
Go beyond plain gradient descent with momentum, RMSprop, and Adam, and see how adaptive optimizers train a neural network faster on real data
6
Regularizing Neural Networks
Diagnose overfitting in deep networks and fix it with L2 weight decay, implemented from scratch in NumPy on the real Diabetes dataset
Achievement
Complete all 6 lessons to finish the Deep Learning Foundations module.