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TensorFlow also offers a variety of libraries and extensions to advance and extend the models and methods used. [67] For example, TensorFlow Recommenders and TensorFlow Graphics are libraries for their respective functional. [68]
The result, x 2, is a "better" approximation to the system's solution than x 1 and x 0. If exact arithmetic were to be used in this example instead of limited-precision, then the exact solution would theoretically have been reached after n = 2 iterations (n being the order of the system).
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 ...
Haskell is a purely functional programming language. Lazy evaluation and the list and LogicT monads make it easy to express non-deterministic algorithms, which is often the case. Infinite data structures are useful for search trees. The language's features enable a compositional way to express algorithms.
OpenVINO IR [5] is the default format used to run inference. It is saved as a set of two files, *.bin and *.xml, containing weights and topology, respectively.It is obtained by converting a model from one of the supported frameworks, using the application's API or a dedicated converter.
AIML, an XML dialect for creating natural language software agents. [48] Apache Lucene, a high-performance, full-featured text search engine library written entirely in Java. [49] Apache OpenNLP, a machine learning based toolkit for the processing of natural language text. It supports the most common NLP tasks, such as tokenization, sentence ...
It is used in natural language processing and information retrieval (IR). It disregards word order (and thus most of syntax or grammar) but captures multiplicity. The bag-of-words model is commonly used in methods of document classification where, for example, the (frequency of) occurrence of each word is used as a feature for training a ...