Andrej Karpathy's Neural Networks Lectures - Notebooks

This repository is a collection of Jupyter notebooks that closely follow Andrej Karpathy's "Neural Networks: Zero to Hero" lectures on YouTube. Alongside the primary content of the lectures, these notebooks offer added insights and notes on PyTorch, neural networks, the transformer architecture, and more.

You can find the notebooks directly within this repository or open them in Google Colab via the provided links.

Please note: For those interested in coding these projects entirely from scratch, I have created a separate repository dedicated to detailed workbooks that guide you through the process step-by-step. You can find these workbooks in the new Andrej Karpathy Zero-to-Hero Workbooks repository.

Lectures & Notebooks

Lecture 1:

The spelled-out intro to neural networks and backpropagation: building micrograd

• Watch on YouTube
• Part 1
• Part 2
• Solutions to micrograd exercises

Lecture 2:

The spelled-out intro to language modeling: building makemore

• Watch on YouTube
• 

Lecture 3:

Building makemore Part 2: MLP

• Watch on YouTube
• 

Lecture 4:

Building makemore Part 3: Activations & Gradients, BatchNorm

• Watch on YouTube
• 

Lecture 5:

Building makemore Part 4: Becoming a Backprop Ninja

• Watch on YouTube
• 

Lecture 6:

Building makemore Part 5: Building a WaveNet

• Watch on YouTube
• 

Lecture 7:

Let's build GPT: from scratch, in code, spelled out.

• Watch on YouTube
• (first time I worked through this tutorial)
• (second time)
• (third time)

Lecture 8:

Let's build the GPT Tokenizer.

• Watch on YouTube
•