Search Results for "mobile operation system"
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Showing 11 open source projects for "mobile operation system"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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TensorFlow
TensorFlow is an open source library for machine learning
Originally developed by Google for internal use, TensorFlow is an open source platform for machine learning. Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow expresses its computations as dataflow graphs, with each node in the graph representing an operation. ... -
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Text2Code for Jupyter notebook
A proof-of-concept jupyter extension which converts english queries
Text2Code for Jupyter notebook project is a proof-of-concept extension for Jupyter Notebook that allows users to generate Python code directly from natural language queries written in English. The tool is designed to simplify data analysis workflows by enabling users to describe their intended operation in plain language instead of manually writing code. When a user enters a textual command, the extension interprets the request and generates a corresponding Python code snippet that can be inserted into the notebook and executed automatically. The system uses natural language processing techniques to identify the intent of the query, extract relevant variables, and map the request to predefined code templates. ... -
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MediaPipe Solutions
Cross-platform, customizable ML solutions
MediaPipe is an open-source framework developed by Google for building cross-platform machine learning pipelines that process audio, video, and other streaming data in real time. The system provides developers with tools and reusable components that allow them to combine multiple machine learning models with preprocessing and postprocessing logic into efficient perception pipelines. These pipelines can run on a wide variety of platforms including mobile devices, desktop systems, web browsers, and embedded edge devices. ... -
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TensorFlow Lite for Microcontrollers
Infrastructure to enable deployment of ML models
...It targets microcontrollers, DSPs, and other resource-constrained hardware where memory, compute, and power are limited. The project enables on-device inference without depending on an operating system, standard C or C++ libraries, or dynamic memory allocation. It is useful for applications such as wake-word detection, sensor analysis, gesture recognition, anomaly detection, and small vision or audio models. Developers can train or convert models into TensorFlow Lite format and deploy them into embedded firmware. Its main value is bringing practical machine learning to edge devices that are too small for conventional mobile or server runtimes. -
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PySyft
Data science on data without acquiring a copy
...This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data without first putting it all in one (central) place. The Syft ecosystem seeks to change this system, allowing you to write software which can compute over information you do not own on machines you do not have (total) control over. This not only includes servers in the cloud, but also personal desktops, laptops, mobile phones, websites, and edge devices. Wherever your data wants to live in your ownership, the Syft ecosystem exists to help keep it there while allowing it to be used privately. -
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dlib
Toolkit for making machine learning and data analysis applications
...There is no installation or configure step needed before you can use the library. All operating system specific code is isolated inside the OS abstraction layers which are kept as small as possible. -
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Vosk Speech Recognition Toolkit
Offline speech recognition API for Android, iOS, Raspberry Pi
Vosk is an offline open source speech recognition toolkit. It enables speech recognition for 20+ languages and dialects - English, Indian English, German, French, Spanish, Portuguese, Chinese, Russian, Turkish, Vietnamese, Italian, Dutch, Catalan, Arabic, Greek, Farsi, Filipino, Ukrainian, Kazakh, Swedish, Japanese, Esperanto, Hindi, Czech, Polish. More to come. Vosk models are small (50 Mb) but provide continuous large vocabulary transcription, zero-latency response with streaming API,... -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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Easy Machine Learning
Easy Machine Learning is a general-purpose dataflow-based system
...The key barriers come from not only the implementation of the algorithms themselves but also the processing for applying them to real applications which often involve multiple steps and different algorithms. Our platform Easy Machine Learning presents a general-purpose dataflow-based system for easing the process of applying machine learning algorithms to real-world tasks. In the system, a learning task is formulated as a directed acyclic graph (DAG) in which each node represents an operation (e.g. a machine learning algorithm), and each edge represents the flow of the data from one node to its descendants. -
Error to trace to log to deploy. One click. No SSH.AppSignal links every error to the trace, the trace to the log, the log to the deploy that shipped it.
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform... -
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neural network designer
a dbms for neural nets. Chatbots, DTrees, random forests, n-grams,...
This project consists out of a windows based designer application and a library (that can run on multiple platforms, including android) together with several demo applications (including an MVC3 chatbot client and an android application). It is probably best compared to a database management system, but for neural networks instead of relational data. As such, the library is optimized for handling any type of data-size by using advanced streaming and caching algorithms. With the designer,...
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