Search Results for "tensorflow"
Sort By:
Showing 5 open source projects for "tensorflow"
View related business solutions-
DataHub is the leading open-source data catalog helping teams discover, understand, and govern their data assets.Built on an open source foundation with a thriving community of 13,000+ members, DataHub gives you unmatched flexibility to customize and extend without vendor lock-in. DataHub Cloud is a modern metadata platform with REST and GraphQL APIs that optimize performance for complex queries, essential for AI-ready data management and ML lifecycle support.
-
Dragonfly | An In-Memory Data Store without LimitsDragonfly is a drop-in Redis replacement that is designed for heavy data workloads running on modern cloud hardware. Migrate in less than a day and experience up to 25X the performance on half the infrastructure.
-
1
Python Outlier Detection
A Python toolbox for scalable outlier detection
...Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. In total, 17 benchmark datasets are used for comparison, which can be downloaded at ODDS. -
2
Gym
Toolkit for developing and comparing reinforcement learning algorithms
...The gym library provides an easy-to-use suite of reinforcement learning tasks. Gym provides the environment, you provide the algorithm. You can write your agent using your existing numerical computation library, such as TensorFlow or Theano. It makes no assumptions about the structure of your agent, and is compatible with any numerical computation library, such as TensorFlow or Theano. The gym library is a collection of test problems — environments — that you can use to work out your reinforcement learning algorithms. These environments have a shared interface, allowing you to write general algorithms. -
3
Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
Real-ESRGAN ncnn Vulkan is an optimized, cross-platform implementation of Real-ESRGAN using the ncnn neural network inference engine and Vulkan for hardware acceleration. Unlike the standard PyTorch-based Real-ESRGAN code, this variant is written in C/C++ and designed to run efficiently on many platforms (including Windows, Linux, and possibly Android) without requiring heavy frameworks like CUDA or Python. It provides command-line tools for upscaling images with selected models, allowing... -
4
MADDPG
Code for the MADDPG algorithm from a paper
...Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. Researchers can use it to reproduce the experiments presented in the paper, which demonstrate how agents learn behaviors such as coordination, competition, and communication. Although archived, MADDPG remains a widely cited baseline in multi-agent reinforcement learning research and has inspired further algorithmic developments. -
Office Ally: Healthcare Software for Your Medical PracticeService Center by Office Ally is a trusted revenue cycle management platform used by over 65,000 healthcare organizations processing more than 350 million claims annually. With it, providers can verify patient eligibility and benefits, upload and submit claims, correct rejected claims, check claim status, and obtain remits. With multiple claim types and submission options, providers can easily submit claims to any payer from any practice management system. Transactions are secure, ensuring the confidentiality of sensitive patient information. With no needed implementation, providers can quickly and effortlessly streamline their billing processes, increase their financial performance, simplify medical billing, and reduce claim rejections for faster reimbursements.
-
5
Baselines
High-quality implementations of reinforcement learning algorithms
Unlike the other two, openai/baselines is not currently a maintained or prominent repo in the OpenAI organization (and I found no strong reference in OpenAI’s main GitHub). Historically, “baselines” repositories are often used for baseline implementations of reinforcement learning algorithms or reference models (e.g. in the RL domain). If there was an OpenAI “baselines” repo, it might have contained reference implementations for reinforcement learning or model policy baselines to compare new...
- Previous
- You're on page 1
- Next
