Open Source Software Development Software - Page 14
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CURRENNT
CUDA-enabled machine learning library for recurrent neural networks
CURRENNT is a machine learning library for Recurrent Neural Networks (RNNs) which uses NVIDIA graphics cards to accelerate the computations. The library implements uni- and bidirectional Long Short-Term Memory (LSTM) architectures and supports deep networks as well as very large data sets that do not fit into main memory. -
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Caffe
A fast open framework for deep learning
Caffe is an open source deep learning framework that’s focused on expression, speed and modularity. It’s got an expressive architecture that encourages application and innovation, and extensible code that’s great for active development. Caffe also offers great speed, capable of processing over 60M images per day with a single NVIDIA K40 GPU. It’s arguably one of the fastest convnet implementations around. Caffe is developed by the Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and a great community of contributors that continue to make Caffe state-of-the-art in both code and models. It’s been used in numerous projects, from startup prototypes and academic research projects, to large scale industrial applications. -
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Caffe Framework
Caffe, a fast open framework for deep learning
Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR) and by community contributors. Yangqing Jia created the project during his PhD at UC Berkeley. Caffe is released under the BSD 2-Clause license. Expressive architecture encourages application and innovation. Models and optimization are defined by configuration without hard-coding. Switch between CPU and GPU by setting a single flag to train on a GPU machine then deploy to commodity clusters or mobile devices. Extensible code fosters active development. In Caffe’s first year, it has been forked by over 1,000 developers and had many significant changes contributed back. Thanks to these contributors the framework tracks the state-of-the-art in both code and models. -
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Caffe2
Caffe2 is a lightweight, modular, and scalable deep learning framework
Caffe2 is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind. Caffe2 is a deep learning framework that provides an easy and straightforward way for you to experiment with deep learning and leverage community contributions of new models and algorithms. You can bring your creations to scale using the power of GPUs in the cloud or to the masses on mobile with Caffe2’s cross-platform libraries. Modularity and being designed for both scale and mobile deployments are the high-level answers to the first question. In many ways Caffe2 is an un-framework because it is so flexible and modular. The original Caffe framework was useful for large-scale product use cases, especially with its unparalleled performance and well tested C++ codebase. Caffe has some design choices that are inherited from its original use case: conventional CNN applications. -
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A utility to extract data from RDBMSs and convert into .arff file format required by WEKA data mining tool set, both interactive wizard and batch working modes.
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Caire
Content aware image resize library
Caire is a content aware image resize library based on Seam Carving for Content-Aware Image Resizing paper. An energy map (edge detection) is generated from the provided image. The algorithm tries to find the least important parts of the image taking into account the lowest energy values. Using a dynamic programming approach the algorithm will generate individual seams across the image from top to down, or from left to right (depending on the horizontal or vertical resizing) and will allocate for each seam a custom value, the least important pixels having the lowest energy cost and the most important ones having the highest cost. We traverse the image from the second row to the last row and compute the cumulative minimum energy for all possible connected seams for each entry. The minimum energy level is calculated by summing up the current pixel value with the lowest value of the neighboring pixels obtained from the previous row. -
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Project moved to GitHub! https://github.com/carrot2/carrot2 Carrot2 is an Open Source Search Results Clustering Engine. It can automatically organize small collections of documents, e.g. search results, into thematic categories. Carrot2 integrates very well with both Open Source and proprietary search engines.
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The Zeus-Framework is a C++ framework using the paradigm of Cell Computing Model for Linux and Windows. Implements biological behaviours (cloning, genetic algorithmes, ect.). Used for grid computing, distribute systems etc.
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Provides a voice interface for applications via a plug in system. Allows the inclusion of voice recognition in an application with a minimum of effort.
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ChainerRL
ChainerRL is a deep reinforcement learning library
ChainerRL (this repository) is a deep reinforcement learning library that implements various state-of-the-art deep reinforcement algorithms in Python using Chainer, a flexible deep learning framework. PFRL is the PyTorch analog of ChainerRL. ChainerRL has a set of accompanying visualization tools in order to aid developers' ability to understand and debug their RL agents. With this visualization tool, the behavior of ChainerRL agents can be easily inspected from a browser UI. Environments that support the subset of OpenAI Gym's interface (reset and step methods) can be used. -
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Cherokee is a fully extensible and scriptable automation engine for any and all java applets. To make it work for your favorite java applet the most you have to do is write is two classes.
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Chronological Cohesive Units
The experimental source code for the paper
The experimental source code for the paper, "A Novel Recommendation Approach Based on Chronological Cohesive Units in Content Consuming" -
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Cigol (logic, spelled backwards) is a deductive logic solver. It can be embedded inside a parent program or used from the command line. It has absolutely no relationship to Tom Mitchell's book on Machine Learning.
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Claudia Bot Builder
Create chat bots for Facebook Messenger, Slack, Amazon Alexa, etc.
Claudia Bot Builder helps developers create and deploy chat-bots for various platforms in minutes to AWS Lambda. It simplifies the messaging workflows, automatically sets up the correct web hooks, and guides you through configuration steps, so that you can focus on important business problems and not have to worry about infrastructure code. This code is enough to operate bots for all supported platforms. Claudia Bot Builder automatically parses the incoming messages into a common format, so you can handle it easily. It also automatically packages the response into the correct message template for the requesting bot, so you do not have to worry about individual bot protocols. Claudia Bot Builder doesn't have a stand-alone http server in the background (such as Express, Hapi, etc.), instead it uses API Gateway and it's not trivial to simulate similar environment locally. Deploy it with --version test to create a separate test environment directly in AWS Lambda. -
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Clipper is a Python based Shell and Debugger for the CLIPS Expert System (see http://clipsrules.sourceforge.net/). It is currently build on pyGTK and pyCLIPS.
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Clown is a "clustering" framework. It allows you to cluster datasets (in ARFF) format using a number of different clustering algorithms.
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Cluster Networks are a new style of neural simulation / neural network modeling, that models networks of neural populations ("clusters") that transform and transmit information using precisely-timed, graded bursts ("pulses" or "volleys") of firing.
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CoPP is an object-oriented framework for developing algorithms for robot path planning. One of of the design goals is to make it easy to make comparisons between various path planning algorithms.
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CoPT, Corpus Processing Tools, is a set of java classes intended to assist field linguists, NLP researchers and developers, students and software developers in all corpus-related processing.
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. Coach collects statistics from the training process and supports advanced visualization techniques for debugging the agent being trained. Coach supports many state-of-the-art reinforcement learning algorithms, which are separated into three main classes - value optimization, policy optimization, and imitation learning. Coach supports a large number of environments which can be solved using reinforcement learning. -
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Codeball AI
AI Code Review that finds bugs and fast-tracks your code
Codeball is a code review AI that scores pull requests on a grade from 0 (needs careful review) to 1. Use Codeball to add labels to help you focus, auto-approve PRs, and more. The Codeball action is easy to use (sane defaults) and is highly customizable to fit your workflow when needed. Label PRs when you should review them with caution. Stay sharp, don't let the bugs pass through. Identifies and approves or labels safe PRs. Save time by fast-tracking PRs that are easy to review. Fully customizable and programmable with GitHub Actions. Codeball Actions are built on multiple smaller building blocks, that are heavily configurable through GitHub Actions. Codeball uses a deep learning model that has been trained on over 1 million Pull Requests. For each contribution, it considers hundreds of inputs. Codeball is optimized for precision, which means it only approves contributions that it's really confident in. -
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CodiumAI PR-Agent
AI-Powered tool for automated pull request analysis
CodiumAI PR-Agent is an open-source tool aiming to help developers review pull requests faster and more efficiently. It automatically analyzes the pull request and can provide several types of commands. See the Usage Guide for instructions how to run the different tools from CLI, online usage, Or by automatically triggering them when a new PR is opened. You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command in any PR comment. The agent will generate a response based on your command. -
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Coframe
Coframe brings your UX to life with AI-powered optimization
Bring your UX to life with AI-powered optimization and personalization. Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your users. Resulting performance data is fed back in to continuously improve your platform's content. With Coframe, your website or app works for you 24/7, not the other way around. All it takes to get up and running is a few lines of code. Coframe gives you full control and visibility. Our mission is to give every digital interface its own sense of intelligence. -
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Cognidrome
Knowledge-Based Simulation
