Lecture 2: crash course on programming neural networks In the second session, we move beyond Python basics to implement a simple neural network entirely from scratch using Python and a few standard ...

We invite students and researchers to participate in a three-part workshop series focused on the theory and practice of programming neural networks.

Confused by neural networks? Break it down step-by-step as we walk through forward propagation using Python—perfect for beginners and curious coders alike!

This repository contains my implementation of a feed-forward neural network classifier in Python and Keras, trained on the Fashion-MNIST dataset. It closely follows the tutorial by The Clever ...

Liberal Arts at UT offers over 40 majors and many top-ranked graduate programs in the social sciences and humanities taught by 750 faculty.

We present a tutorial for MCMC methods that covers simple Bayesian linear and logistic models, and Bayesian neural networks. The aim of this tutorial is to bridge the gap between theory and ...

Neural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This ...