Neural Networks: Zero to Hero - Let's build GPT from scratch.

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

We following a course created by Andrej Karpathy on building neural networks, from scratch, in code.

We start with the basics of backpropagation and build up to modern deep neural networks, like GPT. In my opinion language models are an excellent place to learn deep learning, even if your intention is to eventually go to other areas like computer vision because most of what you learn will be immediately transferable. This is why we dive into and focus on languade models.

Prerequisites: solid programming (Python), intro-level math (e.g. derivative, gaussian).

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This is seventh event from this series.

We build a Generatively Pretrained Transformer (GPT), following the paper "Attention is All You Need" and OpenAI's GPT-2 / GPT-3. We talk about connections to ChatGPT, which has taken the world by storm. We watch GitHub Copilot, itself a GPT, help us write a GPT (meta :D!) . I recommend people watch the earlier makemore videos to get comfortable with the autoregressive language modeling framework and basics of tensors and PyTorch nn, which we take for granted in this video.

If you was absent on previous you can watch all previous lessons.

Full curse is presented on page:
https://karpathy.ai/zero-to-hero.html

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✅ We will follow course program, discuss code, explain fragments that are unclear and learn together.
✅ After course there will be time to eat dinner making new connections in AI worlds and sharing what you think.

Basically you can just view video at your home, but learning in group you can grasp some ideas faster asking questions and learn deeper explaining something that you understand to others.