Search Results for "learning" - Page 30
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Showing 1132 open source projects for "learning"
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Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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TensorFlow Haskell
Haskell bindings for TensorFlow
The tensorflow-haskell package provides Haskell-language bindings for TensorFlow, giving Haskell developers the ability to build and run computation graphs, machine learning models, and leverage TensorFlow's ecosystem—though it is not an official Google release. As an expedient we use docker for building. Once you have docker working, the following commands will compile and run the tests. Run the install_macos_dependencies.sh script in the tools/ directory. The script installs dependencies via Homebrew and then downloads and installs the TensorFlow library on your machine under /usr/local. ... -
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I3D models trained on Kinetics
Convolutional neural network model for video classification
Kinetics-I3D, developed by Google DeepMind, provides trained models and implementation code for the Inflated 3D ConvNet (I3D) architecture introduced in the paper “Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset” (CVPR 2017). The I3D model extends the 2D convolutional structure of Inception-v1 into 3D, allowing it to capture spatial and temporal information from videos for action recognition. This repository includes pretrained I3D models on the Kinetics dataset, with... -
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X-DeepLearning
An industrial deep learning framework for high-dimension sparse data
X-DeepLearning (XDL for short) is a complete set of deep optimization solutions for high-dimensional sparse data scenarios (such as advertising/recommendation/search, etc.). XDL version 1.2 has been released recently. Performance optimization for large batch/low concurrency scenarios, 50-100% performance improvement in such scenarios. Storage and communication optimization, parameters are automatically allocated globally without manual intervention, and requests are merged to completely... -
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ssbc
Hand-torn wrapped vegetables website
ssbc is the source code repository for the Shousibaocai website, a Chinese project focused on DHT, torrent, magnet, and search engine technology. The project was open-sourced to support technical exchange and learning around distributed hash table crawling and search applications. Its history includes earlier Django-based work and a later Node.js rewrite. The repository includes crawler-related code under a spider directory, reflecting its emphasis on collecting and indexing distributed network data. It is more of a website and search technology project than a reusable library. ... -
Fully Managed MySQL, PostgreSQL, and SQL ServerCloud SQL handles your database ops end to end, so you can focus on your app.
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Coach
Enables easy experimentation with state of the art algorithms
...Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning. -
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flutter-beginners-tutorial
All course files for the Flutter Beginners playlist
...Each lesson branch contains the code state for that point in the tutorial, which helps users inspect examples without falling behind. The repository also notes that cloned projects may require running package installation commands before use. It is intended for people learning Flutter fundamentals rather than developers looking for a production-ready app template. It is useful for practicing Dart, widgets, layouts, navigation, state basics, and beginner mobile app concepts alongside the video course. -
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Dopamine
Framework for prototyping of reinforcement learning algorithms
...For additional details, please see our documentation. We provide a set of Colaboratory notebooks which demonstrate how to use Dopamine. We provide a website which displays the learning curves for all the provided agents, on all the games. -
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NodeGoat
The OWASP NodeGoat project
A deliberately vulnerable Node.js application designed for security training, helping developers understand common web vulnerabilities and how to mitigate them. -
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Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
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NativeScript Documentation
Documentation, API reference, and code snippets for NativeScript
...Building Web, iOS, Android, and Vision Pro apps with a shared codebase (aka, cross-platform apps) Building native platform apps with portable JavaScript skills. Augmenting JavaScript projects with platform API capabilities. AndroidTV and Watch development watchOS development. Learning native platforms through JavaScript understanding. Exploring platform API documentation by trying APIs directly from a web browser without requiring a platform development machine setup. -
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benchm-ml
A benchmark of commonly used open source implementations
This repository is designed to provide a minimal benchmark framework comparing commonly used machine learning libraries in terms of scalability, speed, and classification accuracy. The focus is on binary classification tasks without missing data, where inputs can be numeric or categorical (after one-hot encoding). It targets large scale settings by varying the number of observations (n) up to millions and the number of features (after expansion) to about a thousand, to stress test different implementations. ... -
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The Google Cloud Developer's Cheat Sheet
Cheat sheet for Google Cloud developers
Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,... -
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pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ... -
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AppJoint
Cross module Android development made easy!
AppJoint is a minimalist Android componentization tool that aims to simplify cross-module communication in modularized Android projects. In complex apps broken into multiple modules or “features,” invoking methods or classes across module boundaries can become cumbersome or tightly coupled. AppJoint uses a small set of annotations and a simple runtime API to decouple modules: one module can declare a method interface, annotate it, and other modules can call it dynamically without direct... -
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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. -
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Didact
A DIY guide to build your own React
Didact is an educational JavaScript project that teaches developers how to build a small React-like library from scratch. It accompanies a series of explanatory posts that break down React concepts step by step. The project covers rendering DOM elements, element creation, JSX, virtual DOM behavior, reconciliation, components, state, Fiber-style incremental reconciliation, and hooks. Its goal is not to replace React, but to make React’s internal ideas easier to understand through a compact... -
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AUXPI
A new generation of image beds that integrates multiple APIs
...If you want to build auxpi from the source, you can follow the tutorial below to build it. API v2 version distribution and upload, return all image bed storage links. This project is a program written while learning Go in the process of learning Go. There may be many bugs, unacceptable logic, completely different side effects, and the code cannot be seen directly. -
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captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification... -
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Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
...Replica integrates seamlessly with AI Habitat, Meta’s framework for embodied AI training, enabling large-scale agent simulation and photorealistic rendering for reinforcement learning and robotics. Researchers can use Replica’s ReplicaViewer to interactively explore the 3D scenes. -
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AeroPython
Classical Aerodynamics of potential flow using Python
...The first version ran in Spring 2014 and these Jupyter Notebooks were prepared for that class, with assistance from Barba-group PhD student Olivier Mesnard. In Spring 2015, we revised and extended the collection, adding student assignments to strengthen the learning experience. The course is also supported by an open learning space in the GW SEAS Open edX platform. -
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Java Simple Codes
Simple codes in Java for beginners
This repository provides a set of simple codes to start learning programming. It aims to help new learner to familiarize with basic programming notions and simple algorithms which must be known by every programmer. Every notion or idea is presented in a separate class while the test class provides an example on how to call (or use) this notion. In a first time, no OO programming notions are introduced. -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization of pre-trained models from TensorFlow, Keras and TensorFlow.js. ... -
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Modern JavaScript
All lecture files from the Modern JavaScript (Novice to Ninja) course
Modern JavaScript is a repository of lecture files for The Net Ninja’s Modern JavaScript: Novice to Ninja course on Udemy. It is structured with separate branches for individual lessons, allowing learners to open the exact code associated with a specific part of the course. The repository supports hands-on practice with modern JavaScript concepts rather than acting as a general-purpose library. It is helpful for students who want to compare their own code with course examples. The material... -
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RNStudyNotes
Share some experiences in researching and practicing React Native
...It is aimed at Android, iOS, and front-end developers who want structured reference material while learning React Native. The repository also links to related courses, community resources, and ongoing learning notes. Overall, it functions as a broad knowledge base for developers who want to understand React Native through examples, articles, and field-tested lessons. -
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PythonRobotics
Python sample codes and textbook for robotics algorithms
PythonRobotics is a Python code collection and textbook for learning robotics algorithms through readable examples. It covers practical topics such as localization, mapping, path planning, path tracking, control, SLAM, and autonomous navigation. The project is written to make each algorithm’s core idea easy to understand, rather than hiding the logic behind large frameworks. It keeps dependencies minimal so learners can focus on the math, implementation, and behavior of each robotics method. ...
