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Winning solution for the Kaggle Feedback Prize Challenge.
A simple wrapper to call openAI's chatGPT on the terminal written in Python
Build and share delightful machine learning apps, all in Python. 🌟 Star to support our work!
wandbot is a technical support bot for Weights & Biases' AI developer tools that can run in Discord, Slack, ChatGPT and Zendesk
🦜🔗 Build context-aware reasoning applications
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
31st place silver medal solution to USPPPM Kaggle competition
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Pretrain, finetune and deploy AI models on multiple GPUs, TPUs with zero code changes.
Neural Networks: Zero to Hero
ML Collections is a library of Python Collections designed for ML use cases.
🚀 A simple way to launch, train, and use PyTorch models on almost any device and distributed configuration, automatic mixed precision (including fp8), and easy-to-configure FSDP and DeepSpeed support
🔥 A tool for visualizing and tracking your machine learning experiments. This repo contains the CLI and Python API.
Trainable, memory-efficient, and GPU-friendly PyTorch reproduction of AlphaFold 2
🎖️ 4th place solution in the Feedback Prize Competition🎖️
An unsupervised feature selection technique using supervised algorithms such as XGBoost
a generalist algorithm for cellular segmentation with human-in-the-loop capabilities
Hydra is a framework for elegantly configuring complex applications
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
OpenMMLab Detection Toolbox and Benchmark
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
This repository contains code I wrote for all the competitions that I participated in
Tez is a super-simple and lightweight Trainer for PyTorch. It also comes with many utils that you can use to tackle over 90% of deep learning projects in PyTorch.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
🥈50th place in Bristol-Myers Squibb – Molecular Translation competition🥈