Neural Networks: Zero to Hero - Backpropagation.

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

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 first event from this series.

This is the most step-by-step spelled-out explanation of backpropagation and training of neural networks. It only assumes basic knowledge of Python and a vague recollection of calculus from high school.

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.