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Deep learning for dummies. All the practical details and useful utilities that go into working with real models.
Automatic GPU+CPU memory profiling, re-use and memory leaks detection using jupyter/ipython experiment containers
Build and run containers leveraging NVIDIA GPUs
An open source toolkit for Distributed Deep Reinforcement Learning on real and simulated robots.
An API standard for multi-agent reinforcement learning environments, with popular reference environments and related utilities
This project is a digital human that can talk and listen to you. It uses OpenAI's GPT to generate responses, OpenAI's Whisper to transcript the audio, Eleven Labs to generate voice and Rhubarb Lip …
Jupyter notebooks for the code samples of the book "Deep Learning with Python"
The Patterns of Scalable, Reliable, and Performant Large-Scale Systems
🤗 Evaluate: A library for easily evaluating machine learning models and datasets.
Sim-to-real RL training and deployment tools for the Unitree B1, Go1, and Go2 robots.
BlackHole is a modern macOS audio loopback driver that allows applications to pass audio to other applications with zero additional latency.
The lazier way to manage everything docker
🔥 🔥 🔥 Open Source JIRA, Linear and Asana Alternative. Plane helps you track your issues, epics, and product roadmaps in the simplest way possible.
Ghidra is a software reverse engineering (SRE) framework
cheat allows you to create and view interactive cheatsheets on the command-line. It was designed to help remind *nix system administrators of options for commands that they use frequently, but not …
The platform for building AI from enterprise data
Helping you select an MV* framework - Todo apps for React.js, Ember.js, Angular, and many more
Your AI second brain. Get answers to your questions, whether they be online or in your own notes. Use online AI models (e.g gpt4) or private, local LLMs (e.g llama3). Self-host locally or use our c…
Fully open source, End to End Encrypted alternative to Google Photos and Apple Photos
Learn the Deep Learning for Computer Vision in three steps: theory from base to SotA, code in PyTorch, and space-repetition with Anki
A list of tools for annotating data, managing annotations, etc.