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 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

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)

Built from scratch neural network library using only NumPy

The Deep Learning exercises provided in DataCamp

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

All the assignments of DLFA course IIT KGP

Implementation of Logistic Regression considering a single Neural Network Node

Implementation of a simple neural network with one hidden layer and training with backpropagation

Pytorch/ without Pytorch implementation of mnist handwriting dataset

This Jupyter Notebook contains a separate class of Neural Network.