Deep LearningBeginner1:36 video6 chapters

Neural Networks: The Forward Pass — lecture notes

Stack neurons into layers and connect them. Watch signals flow from input to output and become class probabilities.

0:001. Introduction

Introduction — Neural Networks: The Forward Pass

One neuron can only draw a straight line. Connect many of them in layers, and you get a neural network that can learn remarkably complex patterns.

0:112. A forward pass

A forward pass — Neural Networks: The Forward Pass

This network has four inputs, two hidden layers of six neurons and three outputs. In a forward pass, signals flow from left to right. Each neuron combines the signals from the previous layer, and the output layer produces probabilities: 81 percent cat, 14 percent dog, 5 percent bird.

0:323. Matrix form

Matrix form — Neural Networks: The Forward Pass

In practice we compute a whole layer at once with a matrix. Multiply the inputs by the weight matrix W, add the biases, and apply the activation. A network is just these layers chained together, which is why GPUs, built for matrix maths, are so useful.

0:514. Why layers?

Why layers? — Neural Networks: The Forward Pass

Why use layers? Each layer builds on the one before. Early layers detect simple patterns and later layers combine them into complex ones. In theory, a network with enough neurons can approximate almost any function.

1:075. Going deeper

Going deeper — Neural Networks: The Forward Pass

Here is a deeper network with three hidden layers, deciding whether an email is spam. More layers let the network build more abstract features, as long as we have enough data to train it.

1:226. Recap

Recap — Neural Networks: The Forward Pass

To recap. Networks arrange neurons in layers. The forward pass flows from input to output. Each layer computes f of W x plus b. And depth builds complex features from simple ones.

Key takeaways

A network chains layers that each compute f(Wx + b).
The forward pass carries signals from inputs to outputs.
The output layer often produces class probabilities with softmax.
Depth lets networks compose simple features into complex ones.

Check yourself

In a forward pass, information flows…
Show answer
From input to output — The forward pass computes predictions layer by layer.
What does the weight matrix W contain?
Show answer
All the weights between two layers — Each entry connects one neuron to one neuron in the next layer.
Why are GPUs useful for neural networks?
Show answer
They are fast at matrix operations — Layers are matrix multiplications, which GPUs accelerate.

Go deeper

Multilayer Perceptrons and the Universal Approximation Theorem · The AI Lecture Hall
From Biological Neurons to Artificial Neural Networks · The AI Lecture Hall