anbhimi
AI Software Engineer - Delta Airlines | GSoC Mentor 2022, 2023 @Emory-HITI | GSoC Mentor 2024 @uaanchorage
Delta AirlinesAtlanta
anbhimi's Stars
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
TheAlgorithms/Python
All Algorithms implemented in Python
torvalds/linux
Linux kernel source tree
jlevy/the-art-of-command-line
Master the command line, in one page
keras-team/keras
Deep Learning for humans
kamranahmedse/design-patterns-for-humans
An ultra-simplified explanation to design patterns
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code
500 AI Machine learning Deep learning Computer vision NLP Projects with code
fivethirtyeight/data
Data and code behind the articles and graphics at FiveThirtyEight
dair-ai/ML-YouTube-Courses
📺 Discover the latest machine learning / AI courses on YouTube.
khangich/machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
chiphuyen/machine-learning-systems-design
A booklet on machine learning systems design with exercises. NOT the repo for the book "Designing Machine Learning Systems"
huggingface/tokenizers
💥 Fast State-of-the-Art Tokenizers optimized for Research and Production
speechbrain/speechbrain
A PyTorch-based Speech Toolkit
subbarayudu-j/TheAlgorithms-Python
TheAlgorithms/Python
eugeneyan/ml-surveys
📋 Survey papers summarizing advances in deep learning, NLP, CV, graphs, reinforcement learning, recommendations, graphs, etc.
orrsella/soft-eng-interview-prep
Everything you need to know for a Software Engineering interview
huggingface/awesome-papers
Papers & presentation materials from Hugging Face's internal science day
philipperemy/keras-tcn
Keras Temporal Convolutional Network.
linkedin/detext
DeText: A Deep Neural Text Understanding Framework for Ranking and Classification Tasks
facebookresearch/svoice
We provide a PyTorch implementation of the paper Voice Separation with an Unknown Number of Multiple Speakers In which, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers.
eugeneyan/ml-design-docs
📝 Design doc template & examples for machine learning systems (requirements, methodology, implementation, etc.)
spinlud/py-linkedin-jobs-scraper
Christopher-Thornton/hmni
📛 Fuzzy Name Matching with Machine Learning
Emory-HITI/Niffler
Niffler: A DICOM Framework for Machine Learning and Processing Pipelines.
Emory-HITI/AI-Vengers
gaetandi/cheXpert
TannerGilbert/Model-Interpretation
Overview of different model interpretability libraries.
uaanchorage/GSoC
Alaska Project Ideas, mentored by the researchers and collaborators of University of Alaska and supported by open-source entities and enthusiasts in Alaska.
KathiraveluLab/Diomede
DICOM Telemedicine Toolkit