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JAX is a machine learning framework for transforming numerical functions developed by Google with some contributions from Nvidia. [2] [3] [4] It is described as bringing together a modified version of autograd (automatic obtaining of the gradient function through differentiation of a function) and OpenXLA's XLA (Accelerated Linear Algebra).
Kubeflow is an open-source platform for machine learning and MLOps on Kubernetes introduced by Google.The different stages in a typical machine learning lifecycle are represented with different software components in Kubeflow, including model development (Kubeflow Notebooks [4]), model training (Kubeflow Pipelines, [5] Kubeflow Training Operator [6]), model serving (KServe [a] [7]), and ...
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. A TPU is a programmable AI accelerator designed to provide high throughput of low-precision arithmetic (e.g., 8-bit ), and oriented toward using ...
The Google Brain team contributed to the Google Translate project by employing a new deep learning system that combines artificial neural networks with vast databases of multilingual texts. [21] In September 2016, Google Neural Machine Translation (GNMT) was launched, an end-to-end learning framework, able to learn from a large number of ...
Project IDX is an online integrated development environment (IDE) developed by Google. [2] It is based on Visual Studio Code , and the infrastructure runs on Google Cloud . In addition to including the features, languages and plugins supported by VS Code , it has unique functionality built by Google.
Cirq was developed by the Google AI Quantum Team, and the public alpha was announced at the International Workshop on Quantum Software and Quantum Machine Learning on July 18, 2018. [2] A demo by QC Ware showed an implementation of QAOA solving an example of the maximum cut problem being solved on a Cirq simulator.
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An illustration of main components of the transformer model from the paper "Attention Is All You Need" [1] is a 2017 landmark [2] [3] research paper in machine learning authored by eight scientists working at Google.