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Gemma is a family of lightweight, state-of-the art open models built from research and technology used to create Google Gemini models. They are text-to-text, decoder-only large language models, available in English, with open weights, pre-trained variants, and instruction-tuned variants. For more details, please check out the following links: Gemma on Google AI Gemma on Kaggle Gemma on Vertex AI M
Magika is a novel AI powered file type detection tool that relies on the recent advance of deep learning to provide accurate detection. Under the hood, Magika employs a custom, highly optimized Keras model that only weighs about 1MB, and enables precise file identification within milliseconds, even when running on a single CPU. In an evaluation with over 1M files and over 100 content types (coveri
Typograms (short for typographic diagrams) is a lightweight image format (text/typogram) useful for defining simple diagrams in technical documentation, originally developed here. See it in action here: https://google.github.io/typograms/ Like markdown, typograms is heavily inspired by pre-existing conventions found in ASCII diagrams. A small set of primitives and rules to connect them is defined,
This document is for engineers and researchers (both individuals and teams) interested in maximizing the performance of deep learning models. We assume basic knowledge of machine learning and deep learning concepts. Our emphasis is on the process of hyperparameter tuning. We touch on other aspects of deep learning training, such as pipeline implementation and optimization, but our treatment of tho
Yannis Assael1,*, Thea Sommerschield2,3,*, Brendan Shillingford1, Mahyar Bordbar1, John Pavlopoulos4, Marita Chatzipanagiotou4, Ion Androutsopoulos4, Jonathan Prag3, Nando de Freitas1 1 DeepMind, United Kingdom 2 Ca’ Foscari University of Venice, Italy 3 University of Oxford, United Kingdom 4 Athens University of Economics and Business, Greece * Authors contributed equally to this work Ancient His
Note If you are unfamiliar with differential privacy (DP), you might want to go through "A friendly, non-technical introduction to differential privacy". This repository contains libraries to generate ε- and (ε, δ)-differentially private statistics over datasets. It contains the following tools. Privacy on Beam is an end-to-end differential privacy framework built on top of Apache Beam. It is inte
Overview | Quick install | What does Flax look like? | Documentation This README is a very short intro. To learn everything you need to know about Flax, refer to our full documentation. Flax was originally started by engineers and researchers within the Brain Team in Google Research (in close collaboration with the JAX team), and is now developed jointly with the open source community. Flax is bei
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