Open Source Software Development Software - Page 22
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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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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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Genetic Algorithms Library (in Java)
An API for Java developers who need Genetic Algorithms
This API is intended to help developers use genetic algorithms in their own java applications. GeneticLibrary.zip contains the netbeans project of the API itself. GeneticTrial.zip contains another netbeans project which is an example explaining how to use the API. docs.zip is the documentation of the API. Please take a look there, it is really helpful. I have only two request from who will use this API; USE, UPDATE AND MODIFY IT FREELY JUST NOTE THE ORIGINAL AUTHOR -
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Genetic Programming Classifier is a distributed evolutionary data classification program. It uses the ensemble method implemented under a parallel co-evolutionary Genetic Programming technique.
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Powerful App Monitoring Without Surprise BillsTired of monitoring tools that punish you for scaling? AppSignal offers transparent, predictable pricing with every feature unlocked on every plan. Track errors, monitor performance, detect anomalies, and manage logs across Ruby, Python, Node.js, and more. Trusted by developers since 2012 with free dev-to-dev support. No credit card required to start your 30-day trial.
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Genetic Programming (tree structure) predictor within Weka data mining software for both continuous and classification problems.
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The Distributed Genetic Programming Framework is a scalable Java genetic programming environment. It comes with an optional specialization for evolving assembler-syntax algorithms. The evolution can be performed in parallel in any computer network.
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Genetic Programming in OpenCL is a parallel implementation of genetic programming targeted at heterogeneous devices, such as CPU and GPU. It is written in OpenCL, an open standard for portable parallel programming across many computing platforms.
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Compiler optimization for code that AI generates, reuses similar substrings of code to exponentially reduce the Big-O of compile. At runtime, CodeTree objects breed, rename vars, mutate code and run it instantly. For any realtime compilable language.
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This project aims to test Distributed Artifitial Intelligence in a urban traffic light control. GlobalSyncLocal is used to interact with the SUMO simulator through the TraSMAPI.
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Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
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Gluon CV Toolkit
Gluon CV Toolkit
GluonCV provides implementations of state-of-the-art (SOTA) deep learning algorithms in computer vision. It aims to help engineers, researchers, and students quickly prototype products, validate new ideas and learn computer vision. It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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Gnubert will be a system to use the power of many computers across the internet to solve problems using evolutionary techniques. Any user may define a problem and the system will attempt to solve it.
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GoodByeCatpcha
Solver ReCaptcha v2 Free
An async Python library to automate solving ReCAPTCHA v2 by images/audio using Mozilla's DeepSpeech, PocketSphinx, Microsoft Azure’s, Google Speech and Amazon's Transcribe Speech-to-Text API. Also image recognition to detect the object suggested in the captcha. Built with Pyppeteer for Chrome automation framework and similarities to Puppeteer, PyDub for easily converting MP3 files into WAV, aiohttp for async minimalistic web-server, and Python’s built-in AsyncIO for convenience. -
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Grenade
Deep Learning in Haskell
Grenade is a composable, dependently typed, practical, and fast recurrent neural network library for concise and precise specifications of complex networks in Haskell. Because the types are so rich, there's no specific term level code required to construct this network; although it is of course possible and easy to construct and deconstruct the networks and layers explicitly oneself. Networks in Grenade can be thought of as a heterogeneous list of layers, where their type includes not only the layers of the network but also the shapes of data that are passed between the layers. To perform back propagation, one can call the eponymous function which takes a network, appropriate input, and target data, and returns the back propagated gradients for the network. The shapes of the gradients are appropriate for each layer and may be trivial for layers like Relu which have no learnable parameters. -
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HH-Evolver
A framework for domain-specific, hyper-heuristic evolution
HH-Evolver is a framework for domain-specific, hyper-heuristic evolution. HH-Evolver automates the design of domain-specific heuristics for planning domains. Hyper-heuristics generated by our tool can then be used with combinatorial search algorithms such as A* and IDA* for solving problems of the given domain. -
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HYBRYD
Library written in C with Python API for IPv6 networking
This project is a rewritten of an initial project that I've called GLUE and created in 2005. I'm trying to readapt it for Python 2.7.3 and GCC 4.6.3 The library has to be build as a simple Python extension using >python setup.py install and allows to create different kind of servers, clients or hybryds (clients-servers) over (TCP/UDP) using the Ipv6 Protocol. The architecture of the code is based on brain architecture. Will put an IPv6 adress active available as soon as possible so that you can download pieces of codes. The aim of that coding was to use primary linux commands easily codable and make an object of an IPv6 connection. Moreover, the model is full-state! -
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Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. It preserves Sonnet’s module-based programming model for state management while retaining access to JAX’s function transformations. Haiku can be expected to compose with other libraries and work well with the rest of JAX. Similar to Sonnet modules, Haiku modules are Python objects that hold references to their own parameters, other modules, and methods that apply functions on user inputs. -
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HebbBrain
FEED-FORWARD NETWORK
FEED-FORWARD network. Simple to use, hard to manage. Born to be fast and tiny. AI FEED-FORWARD neural network -
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HeuriStitch
An heuristic image stitcher
An heuristic image stitcher made to test Genetic and PSO based algorithms -
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High-order HMM in Matlab
Implementation of duration high-order hidden Markov model in Matlab.
Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition. -
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Hippal is an integrating GUI-framework for a multimodular symbolic A.I.-system IPAL, which combines A.I. Planning, Inductive Program Synthesis, Analogical Reasoning and Learning. Hippal is client-/server-based and uses the Lili Lisp Interpreter as Centr
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
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Human AI Net
a Human and Artificial Intelligence Network
We are going to teach eachother how to build AI and new kinds of game objects in this network using intuitive dragAndDrop. Its going to be a space for experimenting with fun and useful tools in new ways. Version 0.8.0 has some advanced components that will be working soon. The plan is a massively multiplayer space where we design, evolve, and play with game objects and do AI research together which controls those game objects along with directly playing the games. The main data format is, from xorlisp which is also in progress, immutable binary forest nodes, so if millions of people build that together nobody can damage or change anyone else's data since its all constant. You dont change variables. You create new data that points at existing constant data, as deep as you need it. I have mindmap lists, definitions, and 2 editable properties working that way with 2 kinds of event listeners that work locally. Its not a networked system yet, but the datastructs are ready to scale with it. -
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This program generates customizable hyper-surfaces (multi-dimensional input and output) and samples data from them to be used further as benchmark for response surface modeling tasks or optimization algorithms.
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HyperCLIPS
CLIPS compatible application which allows a high performance execution
HyperCLIPS was designed for high performance, especially with the CLIPS program which have a great deal of numeric calculation or memory allocation, along with keeping almost full compatibility with the original version of CLIPS. You can use HyperCLIPS to run CLIPS programs with no modification. The original version of CLIPS Rule Based Programming Language is available from here. https://sourceforge.net/p/clipsrules/ The misclns4.tst is a good example for it because HyperCLIPS performs 200% - 400% faster than the original version of CLIPS. The CLIPS test suite(including misclns4.tst) are available from here. https://sourceforge.net/p/clipsrules/code/HEAD/tree/branches/63x/test_suite
