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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, AWS, and various command lines.

A game theoretic approach to explain the output of any machine learning model.

? A ranked list of awesome machine learning Python libraries. Updated weekly.

A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.

深度学习入门教程, 优秀文章, Deep Learning Tutorial

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

AutoML library for deep learning

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Automated Machine Learning with scikit-learn

An open source python library for automated feature engineering