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

Deep AI Fundamentals

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

Testing various examples and code for Machine Learning using TensorFlow

Assignments for the Machine Learning course (COL774) at IITD

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

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

Backpropogation algorithm implemented in Python 3

The Deep Learning exercises provided in DataCamp

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)

Built from scratch neural network library using only NumPy

Course work from UCLA's ECE C247 - Neural Networks

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

Numpy NeuralNetworks with Keras like interface

A NumPy-only Multilayer Perceptron (MLP) framework built from scratch to understand neural networks, backpropagation, and optimization algorithms.

All the assignments of DLFA course IIT KGP