Search Results for "asynchronous"
Sort By:
Showing 2 open source projects for "asynchronous"
View related business solutions-
Our Free Plans just got better! | Auth0You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
-
Analyze, Control and Reduce your monthly technology spend across your entire enterprise!WinBill by TeleManagement Technologies is a premier telecom expense management software solution. Easily accessible and equipped with a centralized database of information, WinBill provides businesses with an accurate inventory of all vendors, expenses, and IT services. By leveraging WinBill, you can easily keep track of contracts, effortlessly manage IT budgets, and virtually eliminate late payment fees. Additionally, the solution's flexible reporting tools give users the resources they need to create and schedule reports or have them seamlessly access reporting information through the web. Core features include real-time call collection, toll fraud, real-time socket, network design, and more.
-
1
Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments and... -
2
Deep Reinforcement Learning for Keras
Deep Reinforcement Learning for Keras.
keras-rl implements some state-of-the-art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and...
- Previous
- You're on page 1
- Next
