A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.

A trainable convolutional neural network inside a fragment shader

An implementation of various metaheuristics adapted to train neural networks

For Azimuth ACT course

a scale-free network of applied and foundational AI resources

A multi-layer, feedforward neural network (FNN) implementation in Java

Assignments for the Machine Learning course (COL774) at IITD

This Repository contains the lab Programs for On-Going Soft Computing Lab (CSP 3035) Summer 2025

Testing various examples and code for Machine Learning using TensorFlow

Creating a basic NN from scratch to detect numbers from the MNIST dataset using Numpy

Backpropogation algorithm implemented in Python 3

This gradient decent library has not only a determination function, but it has emotions too, as an add on! (Currently the emotions are anger, surprise and excitement)(By emotions, I JUST MEAN BUILT-IN FUNCTIONS FOR EFFICIENT TRAINING)

The Deep Learning exercises provided in DataCamp

Built from scratch neural network library using only NumPy

Multivariate Classification Using a Feed-Forward Neural Network and Backpropagation.

Numpy NeuralNetworks with Keras like interface

A NumPy-only Multilayer Perceptron (MLP) built from first principles to demystify neural networks, backpropagation, and optimization algorithms.

An Automatic Differentiation engine made from scratch.

Course work from UCLA's ECE C247 - Neural Networks