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Q&A: How mathematics can reveal the depth of deep learning AI

Artificial intelligence (AI) is increasingly prevalent, integrated into phone apps, search engines and social media platforms as well as supporting myriad research applications. Of particular interest ...
BIOENGINEER

Oxford team revives simple math to outsmart deep learning in market forecasting

A new Oxford study introduces OFTER, an online forecasting pipeline that combines ARIMA models, PCA-based dimensionality ...
I Programmer

Mathematics of Deep Learning: An Introduction (De Gruyter)

This book sets out to provide a mathematical perspective on some key elements of deep neural networks (DNNs). Leonid Berlyand and Pierre-Emmanuel Jabin's compact textbook offers a view that emphasizes ...
The Brooklyn Paper

Math Reimagined: Achievement First’s Approach to Deep Learning and Student-Led Problem Solving

In recent decades, K-12 math education has evolved significantly, shifting from rote memorization to fostering conceptual understanding and problem-solving skills. Achievement First (AF), a network of ...
I Programmer

Math for Deep Learning (No Starch Press)

This book, subtitled "What You Need to Know to Understand Neural Networks" provides the essential math to follow deep learning discussions, explore more complex implementations, and better use the ...
umass

Jiequn Han: Deep Picard Iteration: Solving High-Dimensional PDEs via Regression-Based Learning

Abstract: Deep learning has opened new possibilities for solving high-dimensional partial differential equations (PDEs), a longstanding challenge due to the notorious "curse of dimensionality." Early ...
umass

Konstantinos Spiliopoulos: Normalization effects on deep neural networks and deep learning for scientific problems

We study the effect of normalization on the layers of deep neural networks. A given layer $i$ with $N_{i}$ hidden units is normalized by $1/N_{i}^{\gamma_{i}}$ with ...