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.

Built from scratch neural network library using only NumPy

An implementation of various metaheuristics adapted to train neural networks

Visualization Tool For Keras

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

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

A Selection of Artificial Sudoku Solvers

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)

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

All the assignments of DLFA course IIT KGP

Pytorch/ without Pytorch implementation of mnist handwriting dataset

A trainable convolutional neural network inside a fragment shader

This Jupyter Notebook contains a separate class of Neural Network.

Testing various examples and code for Machine Learning using TensorFlow

Implementation of Logistic Regression considering a single Neural Network Node

For Azimuth ACT course

Numpy NeuralNetworks with Keras like interface

Backpropogation algorithm implemented in Python 3

Assignments for the Machine Learning course (COL774) at IITD