[1] within a subdiscipline in machine learning, advances in the field of deep learning have allowed neural networks, a class of statistical algorithms. [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 The library numpy can be used for manipulating arrays, scipy for scientific and mathematical. These include software libraries, frameworks, platforms, and tools used for machine learning, deep learning, natural language processing, computer vision, reinforcement learning, artificial general intelligence, and more. The following outline is provided as an overview of, and topical guide to, machine learning
Machine learning (ml) is a subfield of artificial intelligence within computer science that evolved from the study of pattern recognition and computational learning theory [1] in 1959, arthur samuel defined machine learning as a field of study that gives computers the ability to learn without being. Chainer is an open source deep learning framework written purely in python on top of numpy and cupy python libraries The development is led by japanese venture company preferred networks in partnership with ibm, intel, microsoft, and nvidia (1993) fast mars, stanford university department of statistics, technical report 110 Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning
Tensorflow is a software library for machine learning and artificial intelligence It can be used across a range of tasks, but is used mainly for training and inference of neural networks [3][4] it is one of the most popular deep learning frameworks, alongside others such as pytorch It was developed by the google.
OPEN
