ktrain: A Low-Code Library for Augmented Machine Learning

April 19, 2020 ยท Declared Dead ยท ๐Ÿ› Journal of machine learning research

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Authors Arun S. Maiya arXiv ID 2004.10703 Category cs.LG: Machine Learning Cross-listed cs.CL, cs.CV, cs.SI Citations 162 Venue Journal of machine learning research Last Checked 2 months ago
Abstract
We present ktrain, a low-code Python library that makes machine learning more accessible and easier to apply. As a wrapper to TensorFlow and many other libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to make sophisticated, state-of-the-art machine learning models simple to build, train, inspect, and apply by both beginners and experienced practitioners. Featuring modules that support text data (e.g., text classification, sequence tagging, open-domain question-answering), vision data (e.g., image classification), graph data (e.g., node classification, link prediction), and tabular data, ktrain presents a simple unified interface enabling one to quickly solve a wide range of tasks in as little as three or four "commands" or lines of code.
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