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An opinionated list of Python frameworks, libraries, tools, and resources
🤗 Transformers: the model-definition framework for state-of-the-art machine learning models in text, vision, audio, and multimodal models, for both inference and training.
A curated list of awesome Machine Learning frameworks, libraries and software.
PyTorch Tutorial for Deep Learning Researchers
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials,…
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
Zulip server and web application. Open-source team chat that helps teams stay productive and focused.
Python composable command line interface toolkit
Backup and keep your application settings in sync.
🏄 Scalable embedding, reasoning, ranking for images and sentences with CLIP
Modular visual interface for GDB in Python
Awesome & interesting talks about programming
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv
macOS development environment setup: Easy-to-understand instructions with automated setup scripts for developer tools like Vim, Sublime Text, Bash, iTerm, Python data analysis, Spark, Hadoop MapRed…
Facilitating the design, comparison and sharing of deep text matching models.
Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.
Python library that makes it easy for data scientists to create charts.
news-please - an integrated web crawler and information extractor for news that just works
Public domain cross platform lock free thread caching 16-byte aligned memory allocator implemented in C
Turn Python scripts into handouts with Markdown and figures
Save your dotfiles once, deploy them everywhere
A fast, efficient universal vector embedding utility package.
A Collection of BM25 Algorithms in Python
Live training loss plot in Jupyter Notebook for Keras, PyTorch and others
Efficient Counter that uses a limited (bounded) amount of memory regardless of data size.