Build a neural network from scratch

After this lesson you can: Write a working network and its backward pass in plain Python.

First: Backpropagation, intuitively, Python crash course

Watch

• The spelled-out intro to neural networks and backpropagation: building micrograd - Andrej Karpathy, 146 min. A canonical scratch-build lecture focused exactly on neural networks, backpropagation, and plain-code understanding.
• The spelled-out intro to language modeling: building makemore - Andrej Karpathy, 118 min. A strong follow-up lecture applying scratch-built neural-network ideas to a concrete language-modeling task.

Notes

Building a neural network from scratch means you stop treating it as a magic library call. A network is just a chain of simple numerical operations: multiply inputs by weights, add biases, pass results through nonlinear functions, compute a loss, and adjust the weights to reduce that loss. If you can write these steps in Python, you can understand what libraries like PyTorch are automating for you.

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