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scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
He joined Cogent Labs, a Japanese Deep Learning/AI company, in 2017. [4] He is a Machine Learning Engineering Manager at Mercari, Inc. [5] Cournapeau has also been involved in the development of other central numerical Python libraries: NumPy and SciPy. [6] [7]
He is a founder and former chair of the Machine Learning Department at CMU. [4] Mitchell is known for his contributions to the advancement of machine learning, artificial intelligence, and cognitive neuroscience and is the author of the textbook Machine Learning. He is a member of the United States National Academy of Engineering since 2010.
Chollet is the author of Xception: Deep Learning with Depthwise Separable Convolutions, [9] which is among the top ten most cited papers in CVPR proceedings at more than 18,000 citations. [10] Chollet is the author of the book Deep Learning with Python, [11] which sold over 100,000 copies, and the co-author with Joseph J. Allaire of Deep ...
Conformal prediction (CP) is a machine learning framework for uncertainty quantification that produces statistically valid prediction regions (prediction intervals) for any underlying point predictor (whether statistical, machine, or deep learning) only assuming exchangeability of the data. CP works by computing nonconformity scores on ...
A Byte of Python: Author: Swaroop C H: Software used: DocBook XSL Stylesheets with Apache FOP: Conversion program: Apache FOP Version 1.1: Encrypted: no: Page size: 595.275 x 841.889 pts (A4) Version of PDF format: 1.4
Google JAX is a machine learning framework for transforming numerical functions. [ 71 ] [ 72 ] [ 73 ] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and TensorFlow's XLA (Accelerated Linear Algebra).
Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. [1] AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment.