Popular GitHub Repositories for Adam

??? 60+ Implementations/tutorials of deep learning papers with side-by-side notes ?; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), ? reinforcement learning (ppo, dqn), capsnet, distillation, ... ?

? Lion, new optimizer discovered by Google Brain using genetic algorithms that is purportedly better than Adam(w), in Pytorch

A PyTorch Toolbox for Face Recognition

Code for Adam-mini: Use Fewer Learning Rates To Gain More https://arxiv.org/abs/2406.16793

Effortless plugin and play Optimizer to cut model training costs by 50%. New optimizer that is 2x faster than Adam on LLMs.

ADAM - A Question Answering System. Inspired from IBM Watson

Deep learning library in plain Numpy.

This is a repo for my master thesis research about the Fusion of Visual SLAM and GPS. It contains the research paper, code and other interesting data.

Pytorch LSTM RNN for reinforcement learning to play Atari games from OpenAI Universe. We also use Google Deep Mind's Asynchronous Advantage Actor-Critic (A3C) Algorithm. This is much superior and efficient than DQN and obsoletes it. Can play on many games

A Deep Learning and preprocessing framework in Rust with support for CPU and GPU.

CS F425 Deep Learning course at BITS Pilani (Goa Campus)

Google Street View House Number(SVHN) Dataset, and classifying them through CNN

PyTorch implementation of StarSpace as described in "StarSpace: Embed All The Things!" by Ledell Wu, Adam Fisch, Sumit Chopra, Keith Adams, Antoine Bordes and Jason Weston

Toy implementations of some popular ML optimizers using Python/JAX

This repository contains the PyTorch implementation of Zeroth Order Optimization Based Adversarial Black Box Attack (https://arxiv.org/abs/1708.03999)

A collection of various gradient descent algorithms implemented in Python from scratch

A compressed adaptive optimizer for training large-scale deep learning models using PyTorch

Modified XGBoost implementation from scratch with Numpy using Adam and RSMProp optimizers.

The project aimed to implement Deep NN / RNN based solution in order to develop flexible methods that are able to adaptively fillin, backfill, and predict time-series using a large number of heterogeneous training datasets.

[CVPR 2024] Official PyTorch Implementation for Adam