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Python is a high-level, general-purpose programming language that is popular in artificial intelligence. [1] It has a simple, flexible and easily readable syntax. [2] Its popularity results in a vast ecosystem of libraries, including for deep learning, such as PyTorch, TensorFlow, Keras, Google JAX.
Self-GenomeNet is an example of self-supervised learning in genomics. [18] Self-supervised learning continues to gain prominence as a new approach across diverse fields. Its ability to leverage unlabeled data effectively opens new possibilities for advancement in machine learning, especially in data-driven application domains.
Python is a high-level, general-purpose programming language. Its design philosophy emphasizes code readability with the use of significant indentation. [33] Python is dynamically type-checked and garbage-collected. It supports multiple programming paradigms, including structured (particularly procedural), object-oriented and functional ...
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 ...
PyTorch, an open-source Tensor and Dynamic neural network in Python. [84] TensorFlow, an open-source software library for machine learning. [85] Theano, a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. [86]
Self-published, 2011. Learn SQL the Hard Way. Self-published, 2011. Learn Python the Hard Way: A Very Simple Introduction to the Terrifyingly Beautiful World of Computers and Code 3rd edition. Republished under Addison-Wesley Professional, 2013. ISBN 978-0321884916; Learn Ruby the Hard Way: A Simple and Idiomatic Introduction to the Imaginative ...
Unsupervised learning is a framework in machine learning where, in contrast to supervised learning, algorithms learn patterns exclusively from unlabeled data. [1] Other frameworks in the spectrum of supervisions include weak- or semi-supervision , where a small portion of the data is tagged, and self-supervision .
JAX is a machine learning framework for transforming numerical functions. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).