Neural Networks: Zero to Hero - Let's build the GPT Tokenizer.

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 eight event from this series.

The Tokenizer is a necessary and pervasive component of Large Language Models (LLMs), where it translates between strings and tokens (text chunks). Tokenizers are a completely separate stage of the LLM pipeline: they have their own training sets, training algorithms (Byte Pair Encoding), and after training implement two fundamental functions: encode() from strings to tokens, and decode() back from tokens to strings. In this lecture we build from scratch the Tokenizer used in the GPT series from OpenAI. In the process, we will see that a lot of weird behaviors and problems of LLMs actually trace back to tokenization. We'll go through a number of these issues, discuss why tokenization is at fault, and why someone out there ideally finds a way to delete this stage entirely.

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.