Search Results for "mlm level software"
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Showing 5 open source projects for "mlm level software"
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With the world of work changed forever, it’s essential to manage your workplace and assets together to effectively create a high-performing environment. The Eptura experience combines the power of workplace management software with asset management, enabling you to effectively operate your building and facilitate hybrid work.
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Tigerlab insurance software is a powerful, modular, and easily integrated system, built for you on cutting edge technologies. Simplifying complex processes and allowing for automation and configurability, it performs seamlessly on all digital platforms. Our unique business model allows you to invest in best-of-breed solution through a low-risk partnership. Returns are guaranteed whether you choose our individual modules or go full-suite.
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DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop... -
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Petastorm
Petastorm library enables single machine or distributed training
... Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. It can also be used from pure Python code. A dataset created using Petastorm is stored in Apache Parquet format. On top of a Parquet schema, petastorm also stores higher-level schema information that makes multidimensional arrays into a native part of a petastorm dataset. Petastorm supports extensible data codecs. These enable a user to use one of the standard data compressions (jpeg, png) or implement her own. -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
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TensorLayer
Deep learning and reinforcement learning library for scientists
TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extensive collection of customizable neural layers to build advanced AI models quickly, based on this, the community open-sourced mass tutorials and applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. This project can also be found at OpenI and Gitee. 3.0.0 has been pre-released, the current version... -
Get anytime, anywhere access to your bank or credit union’s financial performance. Get insights into your bank or credit union’s financial performance from anywhere with secure, cloud-based access. Get margin components, branch performance, forecasts and more with point-and-click simplicity. Banker’s Dashboard and Credit Union dashboard integrate seamlessly with your core processor. Quick and easy setup, so you can start improving your bottom line right away. Automate reporting and eliminate errors, freeing you to focus on more value-added tasks. Quickly run and revise multiple forecast scenarios, so you can analyze variances and alternative strategies. Measure and compare branch performance. Institute best practices and hold each branch accountable for better results.
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TFLearn
Deep learning library featuring a higher-level API for TensorFlow
TFlearn is a modular and transparent deep learning library built on top of Tensorflow. It was designed to provide a higher-level API to TensorFlow in order to facilitate and speed up experimentations while remaining fully transparent and compatible with it. Easy-to-use and understand high-level API for implementing deep neural networks, with tutorials and examples. Fast prototyping through highly modular built-in neural network layers, regularizers, optimizers, and metrics. Full transparency...
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