In 1989, Yann LeCun helped show that neural networks could read handwritten ZIP codes, laying groundwork for systems that later read bank checks

In 1989, a computer scientist tackled the messy challenge of reading handwritten zip codes for the US Post Office. This ...
IEEE

Enhancing Explainability of Neural Networks Through Architecture Constraints

Abstract: Prediction accuracy and model explainability are the two most important objectives when developing machine learning algorithms to solve real-world problems. Neural networks are known to ...
Microsoft

How does generative AI work?

Get an overview of generative AI, how it works, and how it’s poised to shape the future. Generative AI refers to a class of AI models, such as the GPT series or Llama, that analyzes large amounts of ...
note

[Manifolds and Deep Learning] It is interesting to view backpropagation from the perspective of differential geometry

Why does the gradient propagate in the "reverse" direction? Backpropagation can be understood using only the chain rule from high school mathematics. However, the concepts of differential geometry are ...
IEEE

Yiqi Liu

Root Mean Square Error,Long Short-term Memory,Neural Network,Attention Mechanism,Model Predictive Control,Recurrent Neural Network,Control Input,Convolutional Neural Network,Deep Learning,Model ...
GitHub

Deep Learning Framework from Scratch

A lightweight deep learning framework built entirely with NumPy. This project implements the core components of modern neural networks including forward propagation, backpropagation, optimizers, ...
GitHub

network.py

simple, easily readable, and easily modifiable. It is not optimized, and omits many desirable features.