Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers

ConvNN is used to predict digits for the MNIST dataset

This is a reposatory for implementation of different types of optimizers (SGD, RMSprop, Adam etc.) with three different use cases Function Approximation, Multi-class Single-label Classification and Multi-class Multi-label Classification)

This GitHub repository contains the code used for CS-671: Introduction to Deep Learning course offered by IIT Mandi during the Even Semester of 2022. The repository includes the implementations of various deep learning algorithms and techniques covered in the course.

🧑‍🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

Code to conduct experiments for the paper Modified Gauss-Newton method for solving a smooth system of nonlinear equations.

Fused 4bit AdamW in Cuda

An electrical grid simulator to calculate the least grid cost using optimizers from nevergrad package.

Neural Network implemented with different Activation Functions i.e, sigmoid, relu, leaky-relu, softmax and different Optimizers i.e, Gradient Descent, AdaGrad, RMSProp, Adam. You can choose different loss functions as well i.e, cross-entropy loss, hinge-loss, mean squared error (MSE)

Lion - EvoLved Sign Momentum w/ New Optimizer API in TensorFlow 2.11+

Cardis Optimizer is a simple but complex optimizer that will have your pc running better in minutes!

implementation of sophia (Second-Order cliPped stocHastic optimizAtion)

Implementation and comparison of SGD, SGD with momentum, RMSProp and AMSGrad optimizers on the Image classification task using MNIST dataset

Benchmarking Optimizers for Sign Language detection

This is an application for showing how optimization algorithms work

Evaluating optimization algorithms in IVHD method (interactive visualization of high-dimensional data)

Python Library for creating and training CNNs. Implemented from scratch.

ML with C#

Tutorials on optimizers for deep neural networks

0th order optimizers, gradient chaining, random gradient approximation