Neural Networks & Deep Learning
This course takes the one idea you met at the end of Machine Learning Foundations — a model is a weighted sum trained by gradient descent — and grows it into a deep neural network. You will start from a single neuron, stack neurons into layers, watch a network make a forward pass, then train it live: gradient descent, backpropagation, the effect of depth, and the discipline that stops a network from memorizing instead of learning.
Every module runs entirely in your browser and is extensively hands-on: you will adjust a neuron’s weights and observe its output change, bend a decision boundary with an activation function, vary the learning rate and observe the loss curve converge or diverge, step through backpropagation one gradient at a time, and train a real network on a spiral dataset until it separates the two classes. Each module also presents the corresponding Keras / PyTorch code for later recognition. Each module concludes with a short mastery check; passing it marks the module complete.
The building block
Module 1From a Neuron to a Network
The artificial neuron: a weighted sum plus a bias plus an activation. Activity: drag the weights and bias and watch one neuron act as an AND / OR gate. AI anchor: every deep model is millions of these.
Module 2Activation Functions
Sigmoid, tanh, ReLU — and why a network without them is equivalent to a single linear model. Activity: plot each activation and stack two layers to see non-linearity produce a curved boundary. AI anchor: ReLU and the resurgence of deep learning.
Putting neurons together
Module 3The Forward Pass
Stack neurons into layers and push data through. Inputs → hidden layer → output, and the decision regions a small network can draw. Activity: feed points through a live 2-layer network and watch every value light up. AI anchor: inference.
Module 4Gradient Descent in Practice
How a network learns: a loss to minimize, a learning rate to tune, and a training loop. Activity: train a real net live, watch the loss curve fall, and crank the learning rate until it overshoots. AI anchor: every model is trained this way.
Module 5Backpropagation Intuition
The chain rule, working backward to give every weight its share of the blame. Activity: step one training iteration — forward to the loss, backward through the gradients — and watch which weights move most. AI anchor: the algorithm behind all of it.
What makes it deep learning
Module 6What Depth Buys You
Why stack layers at all? Features built on features. Activity: train a net on a tangled spiral and add hidden layers with a depth slider — watch a boundary no straight line could ever draw appear. AI anchor: representation learning.
Module 7Training Real Networks
Large networks tend to memorize their training data. Activity: observe training loss decrease while validation loss increases, then enable weight decay, dropout, and early stopping and observe the gap close. AI anchor: every production model must address this.
Project
Module 8 · ProjectTrain a Network End-to-End
Put it all together: pick a dataset, set the architecture and learning rate, click Train, and drive a network from random noise to a confident classifier — reading the loss curve and accuracy as it learns. A synthesis check ties every module together.
Capstone
CapstoneBuild a Concept Manipulative
Put it all together: build a single-page interactive that teaches one Deep Learning concept, then submit it for grading and your certificate.