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Understanding Backpropagation

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AI & Data Science

Understanding Backpropagation

2 weeks
0 Learners
May 12

A 2-week, depth-focused learning plan to understand the theory and implementation of the backpropagation algorithm, moving from core mathematical principles to a from-scratch implementation in Python.

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W1

Module 1: The Mathematical Engine of Neural Networks

By the end of this module you will be able to explain and implement the core mathematical concepts required for backpropagation, including multivariate calculus (gradients, chain rule) and basic linear algebra.

3 videos58m
3 readings
3 topics
1 homework
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W2

Module 2: Implementing Backpropagation from Scratch

By the end of this module you will be able to build and train a simple two-layer neural network from scratch using only NumPy, correctly implementing the forward pass, loss calculation, and the backpropagation algorithm.

4 videos44m
3 readings
4 topics
1 homework
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