Popular GitHub Repositories for Adam Paradigm

??? 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

:twisted_rightwards_arrows: Neural Network (NN) Streamer, Stream Processing Paradigm for Neural Network Apps/Devices.

Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm

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.

PonderV2: Pave the Way for 3D Foundation Model with A Universal Pre-training Paradigm

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.

An ultra-lightweight autonomous agent framework based on the ReAct paradigm.

An ultra-lightweight autonomous agent framework based on the ReAct paradigm.

An ultra-lightweight agentic AI framework based on the ReAct paradigm.

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

[COLM-2024] List Items One by One: A New Data Source and Learning Paradigm for Multimodal LLMs

End2EndPerception deployment solution based on vision sparse transformer paradigm is open sourced.

Noise-Tolerant Paradigm for Training Face Recognition CNNs [Official, CVPR 2019]

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

Our codebase trials provide an implementation of the Select and Trade paper, which proposes a new paradigm for pair trading using hierarchical reinforcement learning. It includes the code for the proposed method and experimental results on real-world stock data to demonstrate its effectiveness.