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TensorFlow.nn is a module for executing primitive neural network operations on models. [40] Some of these operations include variations of convolutions (1/2/3D, Atrous, depthwise), activation functions (Softmax, RELU, GELU, Sigmoid, etc.) and their variations, and other operations (max-pooling, bias-add, etc.). [40]
In machine learning, the term tensor informally refers to two different concepts for organizing and representing data. 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 space.
Tensor contraction is an operation that reduces a type (n, m) tensor to a type (n − 1, m − 1) tensor, of which the trace is a special case. It thereby reduces the total order of a tensor by two. It thereby reduces the total order of a tensor by two.
Keras was first independent software, then integrated into the TensorFlow library, and later supporting more. "Keras 3 is a full rewrite of Keras [and can be used] as a low-level cross-framework language to develop custom components such as layers, models, or metrics that can be used in native workflows in JAX, TensorFlow, or PyTorch — with ...
Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google for neural network machine learning, using Google's own TensorFlow software. [2] Google began using TPUs internally in 2015, and in 2018 made them available for third-party use, both as part of its cloud infrastructure and by ...
In computing, CUDA is a proprietary [1] parallel computing platform and application programming interface (API) that allows software to use certain types of graphics processing units (GPUs) for accelerated general-purpose processing, an approach called general-purpose computing on GPUs.
Blue Zones researchers have determined that centenarians (people who are 100 years or older) don't necessarily run marathons or frequent the heavy lifting section of the gym, but rather move ...
One particularly widespread approach to computing for data engineering is dataflow programming, in which the computation is represented as a directed graph (dataflow graph); nodes are the operations, and edges represent the flow of data. [9] Popular implementations include Apache Spark, and the deep learning specific TensorFlow.
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