Search results
Results from the WOW.Com Content Network
In machine learning, the term tensor informally refers to two different concepts (i) a way of organizing data and (ii) a multilinear (tensor) transformation. Data may be organized in a multidimensional array (M-way array), informally referred to as a "data tensor"; however, in the strict mathematical sense, a tensor is a multilinear mapping over a set of domain vector spaces to a range vector ...
TensorFlow serves as a core platform and library for machine learning. TensorFlow's APIs use Keras to allow users to make their own machine-learning models. [33] [43] In addition to building and training their model, TensorFlow can also help load the data to train the model, and deploy it using TensorFlow Serving. [44]
Libraries for AI include TensorFlow.js, Synaptic and Brain.js. [6] Julia is a language launched in 2012, which intends to combine ease of use and performance. It is mostly used for numerical analysis, computational science, and machine learning. [6] C# can be used to develop high level machine learning models using Microsoft’s .NET suite. ML ...
Download as PDF; Printable version; In other projects ... Automated Deep Learning Using Neural Network Intelligence: Develop and Design PyTorch and TensorFlow Models ...
Matroid released a book with co-author Bharath Ramsundar, TensorFlow for Deep Learning. [26] It introduces the fundamentals of machine learning through TensorFlow and explains how to use TensorFlow to build systems capable of detecting objects in images, understanding human text, and predicting the properties of potential medicines.
ReRites is described by John Cayley as "one of the most thorough and beautiful" poetic responses to machine learning. [16] The work's influence on the field of electronic literature was acknowledged in 2022, when the work won the Electronic Literature Organization 's Robert Coover Award for a Work of Electronic Literature.
Differentiable programming has found use in a wide variety of areas, particularly scientific computing and machine learning. [5] One of the early proposals to adopt such a framework in a systematic fashion to improve upon learning algorithms was made by the Advanced Concepts Team at the European Space Agency in early 2016.
High-quality labeled training datasets for supervised and semi-supervised machine learning algorithms are usually difficult and expensive to produce because of the large amount of time needed to label the data. Although they do not need to be labeled, high-quality datasets for unsupervised learning can also be difficult and costly to produce ...