xinyutan's Stars
microsoft/AzureML-BERT
End-to-End recipes for pre-training and fine-tuning BERT using Azure Machine Learning Service
google/googletest
GoogleTest - Google Testing and Mocking Framework
eugeneyan/applied-ml
📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.
abseil/abseil-cpp
Abseil Common Libraries (C++)
remzi-arpacidusseau/ostep-code
Code from various chapters in OSTEP (http://www.ostep.org)
huggingface/pytorch-image-models
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (ViT), MobileNetV4, MobileNet-V3 & V2, RegNet, DPN, CSPNet, Swin Transformer, MaxViT, CoAtNet, ConvNeXt, and more
frozenca/ML-Murphy
Complete solutions for exercises and MATLAB example codes for "Machine Learning: A Probabilistic Perspective" 1/e by K. Murphy
facebookresearch/faiss
A library for efficient similarity search and clustering of dense vectors.
skanev/playground
An irrelevant project where I keep various code I wrote while learning
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.
uber/causalml
Uplift modeling and causal inference with machine learning algorithms
benedekrozemberczki/ASNE
A sparsity aware and memory efficient implementation of "Attributed Social Network Embedding" (TKDE 2018).
floodsung/Deep-Learning-Papers-Reading-Roadmap
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
TheEconomist/graphic-detail-data
Data and code behind the Economist's Graphic Detail section.
jnv/lists
The definitive list of lists (of lists) curated on GitHub and elsewhere
alirezadir/Production-Level-Deep-Learning
A guideline for building practical production-level deep learning systems to be deployed in real world applications.
flashlight/flashlight
A C++ standalone library for machine learning
google-research/google-research
Google Research
sindresorhus/awesome
😎 Awesome lists about all kinds of interesting topics
google-deepmind/open_spiel
OpenSpiel is a collection of environments and algorithms for research in general reinforcement learning and search/planning in games.
Google-Health/records-research
prakhar1989/awesome-courses
:books: List of awesome university courses for learning Computer Science!
jina-ai/clip-as-service
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
jwasham/coding-interview-university
A complete computer science study plan to become a software engineer.
autonomio/talos
Hyperparameter Experiments with TensorFlow and Keras
josephmisiti/awesome-machine-learning
A curated list of awesome Machine Learning frameworks, libraries and software.
dmlc/xgboost
Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
aws/amazon-sagemaker-examples
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
pytorch/pytorch
Tensors and Dynamic neural networks in Python with strong GPU acceleration
jax-ml/jax
Composable transformations of Python+NumPy programs: differentiate, vectorize, JIT to GPU/TPU, and more