noodlefrenzy
Principal SDE at @Microsoft with a focus on ML, Deep Learning, and Distributed Systems.
@MicrosoftUnited States
noodlefrenzy's Stars
openai/openai-cookbook
Examples and guides for using the OpenAI API
facebookresearch/detectron2
Detectron2 is a platform for object detection, segmentation and other visual recognition tasks.
pola-rs/polars
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
microsoft/semantic-kernel
Integrate cutting-edge LLM technology quickly and easily into your apps
facebookresearch/audiocraft
Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.
recommenders-team/recommenders
Best Practices on Recommendation Systems
guidance-ai/guidance
A guidance language for controlling large language models.
microsoft/computervision-recipes
Best Practices, code samples, and documentation for Computer Vision.
Mooler0410/LLMsPracticalGuide
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
microsoft/nlp-recipes
Natural Language Processing Best Practices & Examples
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
nteract/nteract
📘 The interactive computing suite for you! ✨
py-why/EconML
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
Machine-Learning-Tokyo/Interactive_Tools
Interactive Tools for Machine Learning, Deep Learning and Math
microsoft/code-with-engineering-playbook
This is the playbook for "code-with" customer or partner engagements
fairlearn/fairlearn
A Python package to assess and improve fairness of machine learning models.
microsoft/Windows-Machine-Learning
Samples and Tools for Windows ML.
Azure-Samples/modern-data-warehouse-dataops
DataOps for the Modern Data Warehouse on Microsoft Azure. https://aka.ms/mdw-dataops.
dslp/dslp
The Data Science Lifecycle Process is a process for taking data science teams from Idea to Value repeatedly and sustainably. The process is documented in this repo.
gsbDBI/ExperimentData
microsoft/knowledge-extraction-recipes-forms
Knowledge Extraction For Forms Accelerators & Examples
hgeorgako/rfortraders
Quantitative Trading with R
ashleymcnamara/artwork
liupeirong/MLOpsManufacturing
Demonstrate samples and good engineering practice for operationalizing machine learning solutions.
ShoCodeJP/ShoCode
A collaborative, community-powered tech summit for innovative engineers to share knowledge and shape the future of software development.
Azadehkhojandi/SpheroChallenges
Sphero challenges
calacademy-research/citations_finder
jakkaj/exllama_devcontainer
A dev container for exllama
MikeHopcroft/Einstein
A secure execution environment for conducting machine learning trials against confidential data.
tbarlow12/python-bootstrap
Basic Python project for a quick start on a new project