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Computer Science Series
2
Introduction to Machine Learning | The Math Behind Neural Nets | Then We Build One
7th session
Hands-on, math-first class in machine and deep learning. We'll start at supervised learning, loss functions, gradient descent, and the chain rule, then work forward and backprop by hand for a tiny model. We'll compare and contrast layer types (linear, conv;
quick tour of transfoerms/RNNs) and training heuristics (like initialization, normalization, regularization, early stopping, and SGD vs. Adam).
Hands-on time: create your toy net using NumPy, recreate it in PyTorch and get it to learn on a very small real dataset. Short whiteboarding blocks, live coding, and frequent check-ins where you get to do it yourself will occur. Colab links will be given out. Python familiarity is preferred, but not required; the math is graspable and is performed so it will stick, though basic knowledge of calculus is required.
AS
Alex S
1 spot left!
Introductory AI and ML course
31st session
In this course, you will learn the basics of AI and ML such as how it works and the theory behind it. You will also make, train, and test some models. I plan to teach this course till around April - May 2025. I plan to add more sessions once I get more responses and to the Google form so I know what to teach: https://docs.google.com/forms/d/19QiwogqM-ripe3A1AQdO6sZgQbd-HvXgp9UtB0T1TiA/edit?edit_requested=true
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AK
Arnav K
6 spots left!