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A Guide to Python Machine Learning and Data Science Frameworks

A Beginner’s Guide to Python Machine Learning Frameworks

All libraries below are free, and most are open-source.

  • Scikit-learn is a toolkit for machine learning, data mining, data analysis. It is built on NumPy, SciPy, and matplotlib. BSD license. Github URL: Scikit-learn
  • TensorFlow is Google’s machine learning framework. Extremely popular. Optimized for GCP. Github URL: Tensorflow
  • Keras, a high-level neural networks API created by Google engineer Francois Chollet. Skymind is the second-largest contributor to Keras after Google. Keras runs on top of other frameworks like TensorFlow. You can perform distributed training on Spark for Keras models using Deeplearning4j. Github URL: Keras
  • SpaCy is a powerful natural language processing framework written in Python. Github URL: SpaCy
  • PyTorch is Facebook’s popular machine learning framework. A current favorite among researchers. It offers dynamic neural networks and an intuitive API. Pytorch is used in Jeremy Howard and Rachel Thomas’s popular machine learning course fast.ai. Github URL: pytorch
  • SKIL is Skymind’s platform for distributed training of machine learning models, tracking machine learning experiments, deploying models to production and managing them over their lifecycle. URL: SKIL
  • Spearmint is a software package to perform Bayesian optimization on hyperparemeters. GitHub URL: Spearmint
  • Horovod - Uber’s distributed training framework for TensorFlow, Keras, PyTorch, and MXNet. Github URL: Horovod
  • Gensim is a natural-language processing library written in Python and created by RaRe technologies. It is notable for its implementation of Mikolov’s word2vec. Github URL: Gensim
  • Chainer is a framework for deep learning models. It was created by the Preferred Networks team in Tokyo. Github URL: Chainer
  • Flair is a natural language processing framework created by Zalando. Github URL: Flair

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