Search Results for "back"
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Showing 11 open source projects for "back"
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Jittor
Jittor is a high-performance deep learning framework
...The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. Printing those variables shows they have the same shape and dtype. -
2
Rasa
Open source machine learning framework to automate text conversations
...With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed. Rasa enables you to build assistants that can do this in a scalable way. Rasa uses Poetry for packaging and dependency management. If you want to build it from the source, you have to install Poetry first. ... -
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Build production-grade data and ML workflows, hassle-free The infinitely scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks. Don’t let friction between development and production slow down the deployment of new data/ML workflows and cause an increase in production bugs. Flyte enables rapid experimentation with production-grade software. Debug in the cloud by iterating on the workflows locally to achieve tighter feedback loops. As your...
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4
tika-python
Python binding to the Apache Tika™ REST services
A Python port of the Apache Tika library that makes Tika available using the Tika REST Server. This makes Apache Tika available as a Python library, installable via Setuptools, Pip and easy to install. To use this library, you need to have Java 7+ installed on your system as tika-python starts up the Tika REST server in the background. To get this working in a disconnected environment, download a tika server file (both tika-server.jar and tika-server.jar.md5, which can be found here) and set... -
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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5
NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. ... -
6
PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. Quantum circuits can be set up to interface with either NumPy,... -
7
Elephas
Distributed Deep learning with Keras & Spark
...Keras Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously. Hyper-parameter optimization with elephas is based on hyperas, a convenience wrapper for hyperopt and keras. -
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PyTorch Transfer-Learning-Library
Transfer Learning Library for Domain Adaptation, Task Adaptation, etc.
...Our code is pythonic, and the design is consistent with torchvision. You can easily develop new algorithms or readily apply existing algorithms. We appreciate all contributions. If you are planning to contribute back bug-fixes, please do so without any further discussion. If you plan to contribute new features, utility functions or extensions, please first open an issue and discuss the feature with us. -
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Keepsake
Version control for machine learning
Keepsake is a Python library that uploads files and metadata (like hyperparameters) to Amazon S3 or Google Cloud Storage. You can get the data back out using the command-line interface or a notebook. -
Stop Storing Third-Party Tokens in Your DatabaseRolling your own OAuth token storage can be a security liability. Token Vault securely stores access and refresh tokens from federated providers and handles exchange and renewal automatically. Connected accounts, refresh exchange, and privileged worker flows included.
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10
Keras TCN
Keras Temporal Convolutional Network
...Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the maximum number of steps back in time from current sample at time T, that a filter from (block, layer, stack, TCN) can hit (effective history) + 1. The receptive field of the TCN can be calculated. Once keras-tcn is installed as a package, you can take a glimpse of what is possible to do with TCNs. Some tasks examples are available in the repository for this purpose. -
11
ExoPlanet
GUI based toolkit for running common Machine Learning algorithms.
ExoPlanet provides a graphical interface for the construction, evaluation and application of a Machine Learning model in predictive analysis. With the back-end built using the numpy and scikit-learn libraries, as a toolkit, ExoPlanet couples fast and well tested algorithms, a UI designed over the Qt4 framework, and graphs rendered using Matplotlib to provide the user with a rich interface, rapid analytics and interactive visuals. ExoPlanet is designed to have a minimal learning curve, allowing researchers to focus on the applicative aspect of Machine Learning rather than their implementation details. ...
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