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jarvis

JARVIS, a system to connect LLMs with ML community

keras

Deep Learning for humans

langchain

⚡ Building applications with LLMs through composability ⚡

langchainjs

langflow

⛓️ LangFlow is a UI for LangChain, designed with react-flow to provide an effortless way to experiment and prototype flows.

learning-to-learn

Learning to Learn in TensorFlow

lightning

Large-scale linear classification, regression and ranking in Python

llm-strategy

Directly Connecting Python to LLMs - Dataclasses & Interfaces <-> LLMs

macaw

Multi-angle c(q)uestion answering

magenta

Magenta: Music and Art Generation with Machine Intelligence

mechanicalsoup

A Python library for automating interaction with websites.

medical-data

mitxocw-6.0001-introduction-to-computer-science-and-programming-in-python

MIT intro course

morphio

A python and C++ library for reading and writing neuronal morphologies

mujoco-py

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3.

musescore

MuseScore is an open source and free music notation software. For support, contribution, bug reports, visit MuseScore.org. Fork and make pull requests!

nerf

Code release for NeRF (Neural Radiance Fields)

nerfstudio

A collaboration friendly studio for NeRFs

ngp_pl

Instant-ngp in pytorch+cuda trained with pytorch-lightning (high quality with high speed, with only few lines of legible code)

open-webui

User-friendly WebUI for LLMs (Formerly Ollama WebUI)

ownai-main

Run your own AI (A fork for exploratory purposes)

project-handwashing-analysis

project-mimic

personal project with e-GAN

project-titanic

Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy. Practice Skills Binary classification Python

project-veryconvolutedtextadventure

project-wordfrequency

Data Science pipeline we'll build in this notebook can be used to visualize the word frequency distributions of any novel that you can find on Project Gutenberg. The natural language processing tools used here apply to much of the data that data scientists encounter as a vast proportion of the world's data is unstructured data and includes a great deal of text.

pysc2

StarCraft II Learning Environment

randominspirationquote-demo

rc-data

Question answering dataset featured in "Teaching Machines to Read and Comprehend

retro

Retro Games in Gym