Search Results for "environment-modules" - Page 29
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Showing 927 open source projects for "environment-modules"
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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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Stop Cyber Threats with VM-Series Next-Gen Firewall on AzureGain integrated visibility across all traffic in a single pass. Deploy Palo Alto Networks VM-Series to determine application identity and content while automating security policy updates via rich APIs.
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Generative Models
Collection of generative models, e.g. GAN, VAE in Pytorch
...These models are widely used in artificial intelligence to generate new data that resembles the training data, such as images, text, or other structured outputs. The repository serves as an educational and experimental environment where users can study how generative models work internally and replicate results from academic research papers. -
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Faster R-CNN
Object detection framework based on deep convolutional networks
...The Faster R-CNN architecture combines a Region Proposal Network (RPN) with a Fast R-CNN style detection network to share convolutional feature maps and thus speed up detection. The repo includes code to train, test, and deploy Faster R-CNN models under the MATLAB / Caffe environment, example configuration files, and model checkpoints. Multiple configuration files for different datasets and architectures. Evaluation scripts for mAP and detection metrics. -
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Learn_Machine_Learning_in_3_Months
This is the code for "Learn Machine Learning in 3 Months"
This repository outlines an ambitious self-study curriculum for learning machine learning in roughly three months, emphasizing breadth, momentum, and hands-on practice. It sequences core topics—math foundations, classic ML, deep learning, and applied projects—so learners can pace themselves week by week. The plan mixes reading, lectures, coding assignments, and small build-it-yourself projects to reinforce understanding through repetition and implementation. Because ML is a wide field, the... -
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anaGo
Bidirectional LSTM-CRF and ELMo for Named-Entity Recognition
...In anaGo, the simplest type of model is the Sequence model. Sequence model includes essential methods like fit, score, analyze and save/load. For more complex features, you should use the anaGo modules such as models, preprocessing and so on. -
Save Up to 91% on Cloud Compute With Spot VMsRun batch jobs at 60-91% off with Spot VMs. Long-running workloads get automatic discounts with sustained use.
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Universe Starter Agent
A starter agent that can solve a number of universe environments
...The repo includes modules like train.py, worker.py, model.py, a3c.py, and envs.py to support training, parallel worker management, policy/critics, and environment wrappers. -
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Deep Reinforcement Learning TensorFlow
TensorFlow implementation of Deep Reinforcement Learning papers
Deep Reinforcement Learning TensorFlow is a comprehensive TensorFlow codebase that implements several foundational deep reinforcement learning algorithms for educational and experimental use. The repository focuses on clarity and modularity so users can study how different RL approaches are built and compare their behavior across environments. It includes implementations of well-known algorithms such as Deep Q-Networks (DQN), policy gradients, and related variants, demonstrating how neural... -
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DeepLearn
Implementation of research papers on Deep Learning+ NLP+ CV in Python
...This repository contains an implementation of the following research papers on NLP, CV, ML, and deep learning. The required dependencies are mentioned in requirement.txt. I will also use dl-text modules for preparing the datasets. If you haven't use it, please do have a quick look at it. CV, transfer learning, representation learning. -
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Toolbox
Piotr's Image & Video Matlab Toolbox
Piotr’s Image & Video MATLAB Toolbox is a general-purpose MATLAB toolbox for image and video processing and vision tasks, offering utilities, filters, detection, feature extraction, and algorithm building blocks. Example and demo scripts for usage (e.g. acfReadme, detector readmes). It augments MATLAB’s native capabilities (not replacing the Image Processing Toolbox) by providing efficient, reusable wrappers and optimized routines. Example and demo scripts for usage (e.g. acfReadme, detector... -
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EvalAI
Evaluating state of the art in AI
...EvalAI lets participants submit code for their agent in the form of docker images which are evaluated against test environments on the evaluation server. During the evaluation, the worker fetches the image, test environment, and model snapshot and spins up a new container to perform the evaluation. -
Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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Show Facebook Computer Vision Tags
Chrome Extension that displays automated image tags from Facebook
...Since Facebook uses a computer-vision model to analyse user-uploaded images and generate alt-text tags for accessibility (e.g., “Image may contain: golf, grass, outdoor and nature”), this extension surfaces those hidden tags directly in the UI—revealing what kind of information Facebook infers about images (objects present, activities being done, environment). The purpose is educational and somewhat cautionary: to help users understand the scope of visual inference and privacy issues. Once installed, the extension overlays those tags on images in the timeline, making visible what is typically hidden metadata. The project is relatively lightweight but has garnered attention due to its privacy transparency angle. -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
...But in theory there shouldn't be too many problems on python2 and CPU. The basic part (the first five chapters) explains the content of PyTorch. This part introduces the main modules in PyTorch and some tools commonly used in deep learning. For this part of the content, Jupyter Notebook is used as a teaching tool here, and readers can modify and run with notebooks and repeat experiments. -
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Deepo
Set up deep learning environment in a single command line
Deepo is a series of Docker images that allows you to quickly set up your deep learning research environment, supports almost all commonly used deep learning frameworks, supports GPU acceleration (CUDA and cuDNN included), also works in CPU-only mode, and works on Linux (CPU version/GPU version), Windows (CPU version) and OS X (CPU version). Their Dockerfile generator that allows you to customize your own environment with Lego-like modules, and automatically resolves the dependencies for you. ... -
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House3D
A Realistic and Rich 3D Environment
House3D is a large-scale virtual 3D simulation environment designed to support research in embodied AI, reinforcement learning, and vision-language navigation. It provides more than 45,000 richly annotated indoor scenes sourced from the SUNCG dataset, covering diverse architectural layouts such as studios, multi-floor homes, and spaces with detailed furnishings and room types. Each environment includes fully labeled 3D objects, allowing agents to perceive and interact with their surroundings through multiple sensory modalities including RGB images, depth maps, semantic segmentation masks, and top-down maps. ... -
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fscaret_shiny
UI for fscaret
User Interface (ui) application which implements the automated feature selection provided by the 'fscaret' package of R-environment. -
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P3: The Portable Unix Programming System
Multi-process homeostatic software agent library
PUPS/P3 facilitates development of multi-process multi-host computations by providing tools to emulate colonies of homeostatic organisms. It permits persistent computation, homeostatic resource protection, and asychronous interprocess communication. -
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NNVM
Open deep learning compiler stack for cpu, gpu
The vision of the Apache NNVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. Compilation of deep learning models into minimum deployable modules. Infrastructure to automatically generates and optimize models on more backend with better performance. Compilation and minimal runtimes commonly unlock ML workloads on existing hardware. Automatically generate and optimize tensor operators on more backends. Need support for block sparsity, quantization (1,2,4,8 bit integers, posit), random forests/classical ML, memory planning, MISRA-C compatibility, Python prototyping or all of the above? ... -
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EVLib
Management and simulation of Electric Vehicles activities
EVLib is a library for the management and the simulation of Electric Vehicle (EV) activities, at a charging station level, within a Smart Grid environment. EVLib provides a simple, yet efficient interface for the management of all major EV related activities such as the charging and dis-charging of batteries, as well as battery swapping. Moreover, a large number of parameters, such as the number of chargers, the waiting queues, and the available energy can be easily con figured. ... -
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Machine-Learning-Flappy-Bird
Machine Learning for Flappy Bird using Neural Network
...Over successive generations, a genetic algorithm evolves the neural networks by selecting high-performing agents and recombining their parameters to produce improved offspring. This process allows the AI agents to gradually learn better strategies for navigating the obstacles and surviving longer in the game environment. -
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Wandora is a general purpose information extraction, management, and publishing environment based on Topic Maps and Java. Wandora has several data storage options, rich data extraction, import and export capabilities and embedded server.
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DeepLearningProject
An in-depth machine learning tutorial
This tutorial tries to do what most Most Machine Learning tutorials available online do not. It is not a 30 minute tutorial that teaches you how to "Train your own neural network" or "Learn deep learning in under 30 minutes". It's a full pipeline which you would need to do if you actually work with machine learning - introducing you to all the parts, and all the implementation decisions and details that need to be made. The dataset is not one of the standard sets like MNIST or CIFAR, you... -
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FARSA
Framework for Autonomous Robotics Simulation and Analysis
...It allow to simulate different robotic platforms (the iCub humanoid and the khepera, e-puck, and marxbot wheeled robots), design the sensory-motor system of the robot/s, design the environment in which the robot/s operate, design the robot neural controller, and adapt the free parameters of the robot. -
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vue-tetris
Use Vue, Vuex to code Tetris
...It includes core gameplay mechanics such as piece rotation, collision detection, line clearing, and score tracking, all implemented within a clean and modular codebase. The design emphasizes performance and responsiveness, ensuring smooth gameplay even within a web environment. It also serves as an educational resource for developers learning Vue.js, illustrating how to structure real-time applications. The project may include additional features such as keyboard controls, game speed adjustments, and visual styling. Overall, vue-tetris combines entertainment with practical frontend development techniques. -
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retina.js
JavaScript helpers for rendering high-resolution image variants
...You can prepare images for as many levels of pixel density as you want and let retina.js dynamically serve the right image to the user. retina.js assumes you are using Apple's prescribed high-resolution modifiers (@2x, @3x, etc) to denote high-res image variants on your server. It also assumes that if you have prepared a variant for a given high-res environment, that you have also prepared variants for each environment below it. For example, if you have prepared 3x variants, retina.js will assume that you have also prepared 2x variants. If the environment does have 3x capabilities, retina.js will serve up the 3x image. It will expect that url to be /images/my_image@3x.png. If the environment has the ability to display images at higher densities than 3x, retina.js will serve up the image of the highest resolution that you've provided, in this case 3x. -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
This repository aims to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high... -
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GDL Editor
Computerized guideline editor for clinical decision support
...It is designed to be natural language- and reference terminology- agnostic by leveraging the designs of openEHR Reference Model and Archetype Model. The tool provides an editing and testing environment capable of generating forms based on the elements defined in the GDL. Requirements: Java Runtime Enviroment (JRE) 1.6 or higher
