Search Results for "marbel-release"
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Showing 25 open source projects for "marbel-release"
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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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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Kubeflow Training Operator
Distributed ML Training and Fine-Tuning on Kubernetes
Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, TensorFlow, XGBoost, MPI, Paddle, and others. -
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spaCy models
Models for the spaCy Natural Language Processing (NLP) library
...It's written from the ground up in carefully memory-managed Cython. If your application needs to process entire web dumps, spaCy is the library you want to be using. Since its release in 2015, spaCy has become an industry standard with a huge ecosystem. Choose from a variety of plugins, integrate with your machine learning stack and build custom components and workflows. -
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OpenCLIP
An open source implementation of CLIP
...This codebase is work in progress, and we invite all to contribute in making it more accessible and useful. In the future, we plan to add support for TPU training and release larger models. We hope this codebase facilitates and promotes further research. -
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PySyft
Data science on data without acquiring a copy
Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines without first obtaining control over those machines. This is very limiting to human collaboration and systematically drives the centralization of data, because you cannot work with a bunch of data... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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crème de la crème of AI courses
This repository is a curated collection of links to various courses
...The project aggregates links to online courses, tutorials, lecture series, and learning materials from universities, research labs, and independent educators. The repository organizes courses by topic, difficulty level, format, and release year, allowing learners to quickly identify relevant material depending on their experience and interests. Topics covered include deep learning, natural language processing, computer vision, large language models, linear algebra, reinforcement learning, and machine learning engineering. Because the repository links to well-known educational content such as university lecture series and professional training materials, it functions as a structured roadmap for individuals who want to develop expertise in artificial intelligence. -
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TensorRT Backend For ONNX
ONNX-TensorRT: TensorRT backend for ONNX
Parses ONNX models for execution with TensorRT. Development on the main branch is for the latest version of TensorRT 8.4.1.5 with full dimensions and dynamic shape support. For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting... -
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CVPR 2025
Collection of CVPR 2025 papers and open source projects
...The list frequently highlights benchmarks, leaderboards, or notable results so readers can assess impact at a glance. Because conference content evolves rapidly, the repository is updated as authors release code or refine readme instructions, keeping the collection timely. For teams planning literature reviews, study groups, or rapid prototyping sprints, it acts as a central index to the year’s most relevant methods with working implementations. -
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GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
...In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the appropriate latest release from the Files, unzip the file, and run the executable for your operating system (Linux or Windows). More info on github repository. -
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Paper-with-Code-of-Wireless-comm
Paper-with-Code-of-Wireless-communication-Based-on-DL
...Wireless communication research has increasingly adopted deep learning techniques to address complex tasks such as channel estimation, resource allocation, signal detection, and modulation classification. However, many academic publications do not release source code, which makes it difficult for new researchers to reproduce results or experiment with the proposed methods. This repository addresses that challenge by organizing a large set of papers and linking them to available implementations and related research resources. -
Go from Code to Production URL in SecondsSkip the Kubernetes configs. Cloud Run handles HTTPS, scaling, and infrastructure automatically. Two million requests free per month.
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
...Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well. The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. -
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Lumixon
This AI can answer any information based questions from the user.
...The AI produces the complete information regarding the user's question and if you wish to search for another new question, you need to close and run the application again. This feature will be changed in the next release 3. Download and extract the files to your desired location and run the exe file in order to run the application. 4. The AI prints the website(s) links if it is unable to get an answer for the user's question. -
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AirSim
A simulator for drones, cars and more, built on Unreal Engine
AirSim is an open-source, cross platform simulator for drones, cars and more vehicles, built on Unreal Engine with an experimental Unity release in the works. It supports software-in-the-loop simulation with popular flight controllers such as PX4 & ArduPilot and hardware-in-loop with PX4 for physically and visually realistic simulations. It is developed as an Unreal plugin that can simply be dropped into any Unreal environment. AirSim's development is oriented towards the goal of creating a platform for AI research to experiment with deep learning, computer vision and reinforcement learning algorithms for autonomous vehicles. ... -
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Alphafold2
Unofficial Pytorch implementation / replication of Alphafold2
...Will be gradually implemented as more details of the architecture is released. Once this is replicated, I intend to fold all available amino acid sequences out there in-silico and release it as an academic torrent, to further science. Deepmind has open sourced the official code in Jax, along with the weights! This repository will now be geared towards a straight pytorch translation with some improvements on positional encoding. lhatsk has reported training a modified trunk of this repository, using the same setup as trRosetta, with competitive results. ... -
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ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
...ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine learning model by running modelfox train with the path to a CSV file and the name of the column you want to predict. The CLI automatically transforms your data into features, trains a number of linear and gradient boosted decision tree models to predict the target column, and writes the best model to a .modelfox file. ... -
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YOLOX
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5
YOLOX is a high-performance anchor-free YOLO, exceeding yolov3~v5 with MegEngine, ONNX, TensorRT, ncnn, and OpenVINO supported. YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. Prepare your own dataset with images and labels first. For labeling images, you can use tools like Labelme or CVAT. One more thing worth noting is that you should also implement pull_item and load_anno method... -
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CleverHans
An adversarial example library for constructing attacks
...We are currently prioritizing implementing attacks in PyTorch, but we very much welcome contributions for all 3 frameworks. In versions v3.1.0 and prior, CleverHans supported TF1; the code for v3.1.0 can be found under cleverhans_v3.1.0/ or by checking out a prior Github release. The library focuses on providing a reference implementation of attacks against machine learning models to help with benchmarking models against adversarial examples. -
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The Deep Review
A collaboratively written review paper on deep learning, genomics, etc
...We will continue to make project preprints available on bioRxiv or another preprint service and aim to continue publishing the finished reviews in a peer-reviewed venue as well. Like the initial release, we are planning for an open and collaborative effort. New contributors are welcome and will be listed as version 2.0 authors. Manubot is a system for writing scholarly manuscripts via GitHub. Manubot automates citations and references, versions manuscripts using git, and enables collaborative writing via GitHub. -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu backend is selected by default, so the above command is equivalent to if a compatible GPU resource is found on the system. ... -
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Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative... -
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This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
...NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in the development version). The goal of libgpuarray is (from the documentation) make a common GPU ndarray (n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. ... -
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Scene is a computer vision framework that performs background subtraction and object tracking, using two traditional algorithms and three more recent algorithms based on neural networks and fuzzy classification rules. For each detected object, Scene sends TUIO messages to one or several client applications. The present release features GPU accelerated versions of all the background subtraction methods and morphological post processing of the object blobs with dilation and erosion filters, implemented in OpenCL. The framework was mainly designed as a toolkit for the rapid development of interactive art projects that explore dynamics of complex environments. ...
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TrackMania Challenge Generator
Challenge generator for racing game 'TrackMania Nations Forever'
A small tool which generates infinite number of different challenges for racing game 'TrackMania Nations Forever'. Currently only "Stadium" environment is supported. Latest release: 1.0.1.0, both English and Chinese version available. -
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The source code of the Brainstormers' RoboCup champion team 2005 has been made publicly available at the end of 2005. That source code release contains also a lot of our results in applying Reinforcement Learning in the simulated soccer domain.
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GAME stays for Generic Architecture based on Multiple Experts. Its main purpose is to make easy prototyping, test and release of prediction systems. Released by IASC group, university of Cagliari
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