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Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
Streamlit — A faster way to build and share data apps.
Ray is a unified framework for scaling AI and Python applications. Ray consists of a core distributed runtime and a set of AI Libraries for accelerating ML workloads.
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
A high-throughput and memory-efficient inference and serving engine for LLMs
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Magenta: Music and Art Generation with Machine Intelligence
PyTorch implementations of Generative Adversarial Networks.
OpenAI Baselines: high-quality implementations of reinforcement learning algorithms
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
Cross-platform lib for process and system monitoring in Python
An educational resource to help anyone learn deep reinforcement learning.
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
Hackable and optimized Transformers building blocks, supporting a composable construction.
🚀 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
Jupyter Notebooks as Markdown Documents, Julia, Python or R scripts
A system for quickly generating training data with weak supervision
NanoDet-Plus⚡Super fast and lightweight anchor-free object detection model. 🔥Only 980 KB(int8) / 1.8MB (fp16) and run 97FPS on cellphone🔥
Model interpretability and understanding for PyTorch
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
A PyTorch Library for Accelerating 3D Deep Learning Research
Sacred is a tool to help you configure, organize, log and reproduce experiments developed at IDSIA.