Backpropagation & Gradient Descent

Summary

The algorithm that computes how much each weight contributed to the error, then adjusts weights to reduce it — the mechanism by which a neural network actually learns from data.

How to Learn This

1Read a conceptual (not fully mathematical) walkthrough of backpropagation.
2Connect this back to Phase 2's gradient descent — backprop just computes the gradients efficiently.
3Train a tiny network and watch the loss decrease over training epochs.

Activation Functions

Building Networks With TensorFlow / Keras

More topics in Deep Learning Fundamentals

Neural Network Basics Activation Functions Building Networks With TensorFlow / Keras Building Networks With PyTorch Convolutional Neural Networks (CNNs)Recurrent Neural Networks (RNNs) & LSTMs Transfer Learning Regularization in Deep Learning (Dropout, Batch Norm)GPUs & Training Basics

Stuck on this topic? Ask an Insider

Get 1:1 guidance from people who've walked this exact path — free on the InsideEdge app.

Download