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PopScribe AI — Introduction to Neural Networks

Live Transcript

10:22...the loss function tells us how wrong

10:22our prediction is. We want to minimize

10:23that. So the gradient gives us direction

10:23and backpropagation computes it layer

10:24by layer using the chain rule. Google

10:24published a paper on this called

10:24TensorFlow that changed everything.

10:25The weight update formula is

AI Notes

Backpropagation & Gradient Descent

Overview

Loss function measures prediction error

Gradient → direction of steepest descent

Chain rule applied layer-by-layer (backward)

Weight Update Rule

w_new = w_old − η · ∂L∂w

η (eta) = learning rate, controls step size

Neural Network Architecture

Data Flow

Forward pass: input → hidden → output

Backward pass: loss → gradients → weight updates

Instant Insights

Key Term

Chain Rule — Derivative of composite functions. In backprop: computes how each layer contributes to total error.

GGoogle DeepMind

AI Research Lab · Founded 2010 · London, UK

In the News

AlphaFold 3 predicts all molecular interactions · 2 days ago

Gemini 2.0 tops coding benchmarks · 1 week ago

Connection

Relates to gradient descent from Week 2. Backprop computes the gradients that gradient descent uses to update weights.

AI Notes

Backpropagation & Gradient Descent

Loss function measures prediction error

Gradient → direction of steepest descent

Chain rule applied layer-by-layer (backward)

Weight Update Rule

w_new = w_old − η · ∂L∂w

Chat with Scribe

You

Can you explain backpropagation like I’m 5?

Scribe

Imagine you’re throwing darts blindfolded. Each throw, a friend tells you “too far left” or “too high.” You adjust a little each time. That’s backpropagation — the network makes a guess, checks how wrong it was, then adjusts. Each “layer” of the network gets its own correction, working backward from the final answer.

You

What’s the difference between that and gradient descent?

Scribe

Great question! They’re partners: backprop figures out which direction to adjust (computes the gradients). Gradient descent decides how big a step to take (uses those gradients to update the weights). The η in your notes controls the step size.

Curious about something? Ask me...Enter

ClozeBloom: Apply

In backpropagation, the ______ is applied layer-by-layer to compute how each weight contributes to the total error.

A rule for differentiating composite functions

Again

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Good

Easy

★ 3 streak7 / 12 cardsSection: Backpropagation

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I’m Brian. I’ve spent 13+ years in tech at companies like GitLab, Fastly, and Intercom. Now I’m studying AI at Laney College in Oakland — and I still can’t take notes fast enough to keep up with what the professor is saying. So I started building something to fix it.

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