Machine learning and deep learning importance are increasing with the increasing size of data sets. The biggest challenge in front of developers is to build models compatible and scalable with the data set size and dimension. TensorFlow is one of the most used software libraries to build such models. The article focuses on the TensorFlow concept, its features, benefits, architecture and Tensorflow Batch Normalisation.
Five years ago, TensorFlow was developed by the Google Brain team for Google’s internal use. It was released under the Apache License 2.0. It is an end-to-end open-source platform of a software library for numerical computations. It consists of flexible and comprehensive tools, community resources and libraries that help researchers build and deploy machine learning applications quickly. It makes machine learning and deep learning more accessible and faster.
TensorFlow runs on multiple CPUs (Central Processing Units) and GPUs (Graphics Processing Units). It carries out general-purpose computing on GPUs with CUDA and SYCL extensions. Stateful dataflow graphs demonstrate computations of TensorFlow. TensorFlow was derived from the multidimensional data array operations performed by neural networks referred to as tensors.
TensorFlow has a flexible architecture that allows easy implementation of machine learning algorithms. Its key features are mentioned as follows:
Thus, TensorFlow provides the perfect framework supporting the scalable production of machine intelligence.
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