Understanding Backpropagation
“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.”
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.
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.
Learn
Watch curated videos and read study resources
Practice
Practice what you learned
Build Projects
Build projects using your new gained knowledge
Submit & Verify
Submit your project and get verified by our system
References
Rate this roadmap
Help the community find verified technical paths.
Community Insights
0Join the discussion
Sign in to share your thoughts and technical insights.
Loading insights...