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In May 2016, Google announced its Tensor processing unit (TPU), an application-specific integrated circuit (ASIC, a hardware chip) built specifically for machine learning and tailored for TensorFlow.
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Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can learn from data and generalize to unseen data, and thus perform tasks without explicit instructions. [1]
Keras is an open-source library that provides a Python interface for artificial neural networks.Keras was first independent software, then integrated into the TensorFlow library, and later supporting more.
A convolutional neural network (CNN) is a regularized type of feedforward neural network that learns features by itself via filter (or kernel) optimization. This type of deep learning network has been applied to process and make predictions from many different types of data including text, images and audio. [1]
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. [1]
Google Tensor is a series of ARM64-based system-on-chip (SoC) processors designed by Google for its Pixel devices. It was originally conceptualized in 2016, following the introduction of the first Pixel smartphone, though actual developmental work did not enter full swing until 2020.
Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).