Starred repositories
An Open Source Machine Learning Framework for Everyone
🤗 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.
Command-line program to download videos from YouTube.com and other video sites
The new Windows Terminal and the original Windows console host, all in the same place!
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Magnificent app which corrects your previous console command.
A topic-centric list of HQ open datasets.
scikit-learn: machine learning in Python
📚 Collaborative cheatsheets for console commands
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
Python Data Science Handbook: full text in Jupyter Notebooks
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
A modern file manager that helps users organize their files and folders.
A toolkit for developing and comparing reinforcement learning algorithms.
Extremely fast Query Engine for DataFrames, written in Rust
⚡ A Fast, Extensible Progress Bar for Python and CLI
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,…
📝 An awesome Data Science repository to learn and apply for real world problems.
Python Fire is a library for automatically generating command line interfaces (CLIs) from absolutely any Python object.
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
A code-searching tool similar to ack, but faster.
A game theoretic approach to explain the output of any machine learning model.