vrozenbaum

vrozenbaum's Stars

• langchain-ai/langchain

  🦜🔗 Build context-aware reasoning applications

  Language:Jupyter Notebook93.6k6857.7k15.1k

• nomic-ai/gpt4all

  GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

  Language:C++70k6411.9k7.7k

• CompVis/stable-diffusion

  A latent text-to-image diffusion model

  Language:Jupyter Notebook67.9k55771210.1k

• SawyerHood/draw-a-ui

  Draw a mockup and generate html for it

  Language:TypeScript13.2k100541.6k

• facebookresearch/ParlAI

  A framework for training and evaluating AI models on a variety of openly available dialogue datasets.

  Language:Python10.5k2831.5k2.1k

• drivendataorg/cookiecutter-data-science

  A logical, reasonably standardized, but flexible project structure for doing and sharing data science work.

  Language:Python8.3k1221742.4k

• bitsandbytes-foundation/bitsandbytes

  Accessible large language models via k-bit quantization for PyTorch.

  Language:Python6.2k501k618

• wireservice/csvkit

  A suite of utilities for converting to and working with CSV, the king of tabular file formats.

  Language:Python6k129919607

• NVIDIA/FasterTransformer

  Transformer related optimization, including BERT, GPT

  Language:C++5.8k62625889

• enricoros/big-AGI

  Generative AI suite powered by state-of-the-art models and providing advanced AI/AGI functions. It features AI personas, AGI functions, multi-model chats, text-to-image, voice, response streaming, code highlighting and execution, PDF import, presets for developers, much more. Deploy on-prem or in the cloud.

  Language:TypeScript5.4k625091.2k

• mherrmann/helium

  Lighter web automation with Python

  Language:Python4.9k7993370

• commonsense/conceptnet5

  Code for building ConceptNet from raw data.

  Language:Roff2.8k173170355

• ishan0102/vimGPT

  Browse the web with GPT-4V and Vimium

  Language:Python2.6k3022197

• Trusted-AI/AIF360

  A comprehensive set of fairness metrics for datasets and machine learning models, explanations for these metrics, and algorithms to mitigate bias in datasets and models.

  Language:Python2.4k90251833

• fairlearn/fairlearn

  A Python package to assess and improve fairness of machine learning models.

  Language:Python1.9k39465416

• tldraw/make-real-starter

  Make it real

  Language:TypeScript1.4k1313242

• EthicalML/awesome-artificial-intelligence-guidelines

  This repository aims to map the ecosystem of artificial intelligence guidelines, principles, codes of ethics, standards, regulation and beyond.

  1.3k610166

• dssg/aequitas

  Bias Auditing & Fair ML Toolkit

  Language:Python6834389113

• ArtLabss/open-data-anonymizer

  Python Data Anonymization & Masking Library For Data Science Tasks

  Language:Python24271029

• pterhoer/MAAD-Face

  A face database with a large number of high-quality attribute annotations

  2019625

• linkedin/LiFT

  The LinkedIn Fairness Toolkit (LiFT) is a Scala/Spark library that enables the measurement of fairness in large scale machine learning workflows.

  Language:Scala16815321

• synthesized-io/fairlens

  Identify bias and measure fairness of your data

  Language:Python899509

• Shikib/usr

  Code for ACL 2020 paper: USR: An Unsupervised and Reference Free Evaluation Metric for Dialog Generation (https://arxiv.org/pdf/2005.00456)

  Language:Python50279

• DIUx-xView/xview3-reference

  Reference data processing code and model for the xView3 prize challenge.

  Language:Jupyter Notebook4411728

• wearepal/EthicML

  Package for evaluating the performance of methods which aim to increase fairness, accountability and/or transparency

  Language:Python234853

• princetonvisualai/imagecaptioning-bias

  Code for the paper "Understanding and Evaluating Racial Biases in Image Captioning"

  Language:Python11123

• seymayucer/VGG1200-Races

  Exploring Racial Bias within Face Recognition via per-subject Adversarially-Enabled Data Augmentation

  Language:Python11413

• bzylich/persona-chatbot

  Language:Python9302

• Peleja/odsc_mlops_from_model_to_prod

  Machine Learning Operations (MLOps) are essential to build successful Data Science use-cases. Today, ML is powering data driven use-cases that are transforming industries around the world. In order to seize and hold it's competitive advantage business needs to reduce risk therefore a new expertise rises to include data science models in operational systems. According to Gartner Research “While many organizations have experimented with AI proofs of concept, there are still major blockers to operationalizing its development. IT leaders must strive to move beyond the POC to ensure that more projects get to production and that they do so at scale to deliver business value. (July 2020)”. In this session, we will discuss the role of MLOps and how they can help data science models from deployment to maintenance with focus on: keep track of performance degradation overtime from model predictions quality, setting up continuous evaluation metrics and tuning the model performance in both training and serving pipelines that are deployed in production.

  Language:HTML81012

• TRSS-Research/SAAC

  Language:Jupyter Notebook3000