Neural Networks: Zero to Hero - Becoming a Backprop Ninja.

Building makemore Part 4: Becoming a Backprop Ninja

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 fifth event from this series.

We take the 2-layer MLP (with BatchNorm) from the previous video and backpropagate through it manually without using PyTorch autograd's loss.backward(): through the cross entropy loss, 2nd linear layer, tanh, batchnorm, 1st linear layer, and the embedding table. Along the way, we get a strong intuitive understanding about how gradients flow backwards through the compute graph and on the level of efficient Tensors, not just individual scalars like in micrograd. This helps build competence and intuition around how neural nets are optimized and sets you up to more confidently innovate on and debug modern neural networks.

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.