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Explore Why Deep Learning Works Unreasonably Well [How Models Learn Part 3] with 383,098 views, 18,735 likes, and 519 comments. Experience the impact of this video content that has captured audience attention.

Take your personal data back with Incogni! Use code WELCHLABS and get 60% off an annual plan: http://incogni.com/welchlabs Welch Labs Guide to AI: https://www.welchlabs.com/resources/ai-book-ezrzm-msrmc New Patreon Rewards 33:31- own a piece of Welch Labs history! https://www.patreon.com/welchlabs Books & Posters https://www.welchlabs.com/resources Sections 0:00 - Intro 4:49 - How Incogni Saves Me Time 6:32 - Part 2 Recap 8:10 - Moving to Two Layers 9:15 - How Activation Functions Fold Space 11:45 - Numerical Walkthrough 13:42 - Universal Approximation Theorem 15:45 - The Geometry of Backpropagation 19:52 - The Geometry of Depth 24:27 - Exponentially Better? 30:23 - Neural Networks Demystifed 31:50 - The Time I Quit YouTube 33:31 - New Patreon Rewards! Special Thanks to Patrons https://www.patreon.com/welchlabs Juan Benet, Ross Hanson, Yan Babitski, AJ Englehardt, Alvin Khaled, Eduardo Barraza, Hitoshi Yamauchi, Jaewon Jung, Mrgoodlight, Shinichi Hayashi, Sid Sarasvati, Dominic Beaumont, Shannon Prater, Ubiquity Ventures, Matias Forti, Brian Henry, Tim Palade, Petar Vecutin, Nicolas baumann, Jason Singh, Robert Riley, vornska, Barry Silverman, Jake Ehrlich, Mitch Jacobs, Lauren Steely, Jeff Eastman, Rodolfo Ibarra, Clark Barrus, Rob Napier, Andrew White, Richard B Johnston, abhiteja mandava, Burt Humburg, Kevin Mitchell, Daniel Sanchez, Ferdie Wang, Tripp Hill, Richard Harbaugh Jr, Prasad Raje, Kalle Aaltonen, Midori Switch Hound, Zach Wilson, Chris Seltzer, Ven Popov, Hunter Nelson, Amit Bueno, Scott Olsen, Johan Rimez, Shehryar Saroya, Tyler Christensen, Beckett Madden-Woods, Darrell Thomas, Javier Soto References Simon Prince, Understanding Deep Learning. https://udlbook.github.io/udlbook/ Liang, Shiyu, and Rayadurgam Srikant. "Why deep neural networks for function approximation?." arXiv preprint arXiv:1610.04161 (2016). Hanin, Boris, and David Rolnick. "Deep relu networks have surprisingly few activation patterns." *Advances in neural information processing systems* 32 (2019). Hanin, Boris, and David Rolnick. "Complexity of linear regions in deep networks." *International Conference on Machine Learning*. PMLR, 2019. Fan, Feng-Lei, et al. "Deep relu networks have surprisingly simple polytopes." *arXiv preprint arXiv:2305.09145* (2023). All Code: https://github.com/stephencwelch/manim_videos 100k neuron wide example training code: https://github.com/stephencwelch/manim_videos/blob/master/_2025/backprop_3/notebooks/Wide%20Training%20Example.ipynb Code from viewer Hugo Brouwer that achieves 99.5%+ with less than 100 neurons using Fourier Features! https://github.com/AgntBrwr/baarle-hertog-fourier-features Code from viewer Nico Waser that uses 100 neurons: https://github.com/Waser2004/Illustrated_-_Guide_to_AI/tree/main/Chapter%204%20-%20Deep%20Learning Written by: Stephen Welch Produced by: Stephen Welch, Sam Baskin, and Pranav Gundu Premium Beat IDs EEDYZ3FP44YX8OWTe MWROXNAY0SPXCMBS CFAQJOTYQHT7JYIT

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