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A repository for research on medium sized language models.
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
Matplotlib styles for scientific plotting
ICLR 2024 OpenReivew Submission Data
An ultimately comprehensive paper list of Vision Transformer/Attention, including papers, codes, and related websites
For optimization algorithm research and development.
A simple but complete full-attention transformer with a set of promising experimental features from various papers
🚀 Accelerate training and inference of 🤗 Transformers and 🤗 Diffusers with easy to use hardware optimization tools
🚀 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
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
Benchmarking Deep Learning operations on different hardware
A resource for learning about Machine learning & Deep Learning
This repo contains implementation of different architectures for emotion recognition in conversations.
Python package built to ease deep learning on graph, on top of existing DL frameworks.
Acceptance rates for the major AI conferences
This repository is to prepare for Machine Learning interviews.
Commented (but unaltered) version of original word2vec C implementation.
Web interface for browsing, search and filtering recent arxiv submissions
Fast Python Collaborative Filtering for Implicit Feedback Datasets
Persists tmux environment across system restarts.
Implicit matrix factorization as outlined in http:https://yifanhu.net/PUB/cf.pdf.
An interactive book on deep learning. Much easy, so MXNet. Wow. [Straight Dope is growing up] ---> Much of this content has been incorporated into the new Dive into Deep Learning Book available at …