Search Results for "algorithms framework" - Page 7
Sort By:
Showing 404 open source projects for "algorithms framework"
View related business solutions-
$300 Free Credits for Your Google Cloud ProjectsLaunch your next project with $300 in free Google Cloud credits—no strings attached. Test, build, and deploy without risk. Use your credits across the entire Google Cloud platform to find what works best for your needs. After your credits are used, continue with always-free tier services. Only pay when you're ready to scale. Sign up in minutes and start exploring.
-
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.
-
1
RL Baselines Zoo
A collection of 100+ pre-trained RL agents using Stable Baselines
RL Baselines Zoo is a comprehensive training framework and collection of pre-trained RL agents using Stable-Baselines3. It offers tools for training, tuning, and evaluating RL algorithms across many standard environments, including MuJoCo, Atari, and robotics simulations. Designed for reproducible RL research and benchmarking, it includes scripts, hyperparameter presets, and best practices for training robust agents. -
2
ScaffCC
Compilation and optimization framework for the Scaffold language
ScaffCC is a compiler and scheduler for the Scaffold programing language. It is written using the LLVM open-source infrastructure. It is for the purpose of writing and analyzing code for quantum computing applications. ScaffCC enables researchers to compile quantum applications written in Scaffold to a low-level quantum assembly format (QASM), apply error correction, and generate time and area metrics. It is written to be scalable up to problem sizes in which quantum algorithms outperform... -
3
frontend-interview
The strongest front-end interview guide in the universe
This is a review summary project about preparing for front-end interviews that I have summarized, and the project is updated from time to time. This is not only a strategy for job interviews, but also a collection of front-end ers to examine themselves and achieve breakthroughs. I hope that through this guide, everyone can open up their own lines of Ren and Du, and go further on the road to the front. This warehouse uses a large number of diagrams to convey knowledge. The so-called picture... -
4
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. ... -
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
5
GnuTLS
Mirror of official gnutls repository
GnuTLS is an open-source secure communications library designed to implement cryptographic protocols such as TLS, SSL, and DTLS for protecting data transmitted over networks. Developed as part of the GNU Project, it provides a robust framework for building secure client-server applications and services that require encrypted communication channels. The library includes implementations of widely used security standards and supports modern encryption algorithms, authentication mechanisms, and certificate management features. GnuTLS is frequently used in network servers, email systems, VPNs, and embedded systems where developers need reliable and standards-compliant TLS functionality. ... -
6
TensorFlow Machine Learning Cookbook
Code for Tensorflow Machine Learning Cookbook
TensorFlow Machine Learning Cookbook repository provides practical code examples and educational materials that accompany the book TensorFlow Machine Learning Cookbook. The repository contains numerous Python scripts and Jupyter notebooks that demonstrate how to implement machine learning algorithms and neural networks using the TensorFlow framework. Each section focuses on a different aspect of machine learning development, including tensor manipulation, model training, optimization strategies, and data processing techniques. The examples illustrate how TensorFlow operations and tensors can be used to build machine learning pipelines and perform tasks such as regression, classification, and clustering. ... -
7
Machine Learning with TensorFlow
Accompanying source code for Machine Learning with TensorFlow
Machine Learning with TensorFlow is an open repository containing the source code and practical examples that accompany the book Machine Learning with TensorFlow. The project provides numerous code samples demonstrating how to build machine learning models using the TensorFlow framework. These examples illustrate core machine learning concepts such as regression, classification, clustering, and neural networks through practical implementations. The repository includes implementations of algorithms such as logistic regression, convolutional neural networks, and autoencoders, which allow readers to experiment with different learning techniques. ... -
8
An open source framework for LC-MS based proteomics and metabolomics. OpenMS offers data structures and algorithms for the processing of mass spectrometry data. The library is written in C++. Our source code and wiki lives on GitHub (https://github.com/OpenMS/OpenMS).
-
9
Coach
Enables easy experimentation with state of the art algorithms
...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. -
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.
-
10
Dopamine
Framework for prototyping of reinforcement learning algorithms
Dopamine is a research framework for fast prototyping of reinforcement learning algorithms. It aims to fill the need for a small, easily grokked codebase in which users can freely experiment with wild ideas (speculative research). This first version focuses on supporting the state-of-the-art, single-GPU Rainbow agent (Hessel et al., 2018) applied to Atari 2600 game-playing (Bellemare et al., 2013). -
11
benchm-ml
A benchmark of commonly used open source implementations
...The benchmarks cover algorithms like logistic regression, random forest, gradient boosting, and deep neural networks, and they compare across toolkits such as scikit-learn, R packages, xgboost, H2O, Spark MLlib, etc. The repository is structured in logical folders, each corresponding to algorithm categories. -
12
TheoremJS
A Math library for computation in JavaScript
TheoremJS is a mathematical framework that is packed up with tons of useful mathematical functions. It is composed of functions in most mathematical fields such as algebra, number theory, statistics, etc... It was designed to fit your need, whatever you are trying to do. With TheoremJS, you won't feel the need of running the code on a server, you can run it right on the browser, or on a NodeJS notebook (Jupyter like). -
13
AIAlpha
Use unsupervised and supervised learning to predict stocks
AIAlpha is a machine learning project focused on building predictive models for financial markets and algorithmic trading strategies. The repository explores how artificial intelligence techniques can analyze historical financial data and generate predictions about asset price movements. It provides a research-oriented environment where users can experiment with data processing pipelines, model training workflows, and quantitative trading strategies. The project typically involves collecting... -
14
Active Learning
Framework and examples for active learning with machine learning model
Active Learning is a Python-based research framework developed by Google for experimenting with and benchmarking various active learning algorithms. It provides modular tools for running reproducible experiments across different datasets, sampling strategies, and machine learning models. The system allows researchers to study how models can improve labeling efficiency by selectively querying the most informative data points rather than relying on uniformly sampled training sets. ... -
15
SSHTOOLS
Java SSH API
This project now hosts the third-generation of Java SSH API, Maverick Synergy. This API builds on the Maverick Legacy commercial APIs and delivers a new API in a unified client/server framework. This API is available to the community under the LGPL open source license. This update includes ed25519 support, support for the new OpenSSH private key file format and stronger key exchange algorithms. The project continues to host both the original API and legacy applications created around it, however, these are now considered deprecated and we do not recommend their use in anyway. -
16
ELF (Extensive Lightweight Framework)
An End-To-End, Lightweight and Flexible Platform for Game Research
ELF (Extensive, Lightweight, and Flexible) is a high-performance platform for reinforcement learning research that unifies simulation, data collection, and distributed training. A C++ core provides fast environments and concurrent actors, while Python bindings expose simple APIs for agents, replay, and optimization loops. It supports both single-agent and multi-agent settings, with batched stepping and shared-memory queues that keep GPUs saturated during training. ELF introduced widely used... -
17
BWAPI
An API for interacting with Starcraft: Brood War (1.16.1)
Brood War Application Programming Interface or BWAPI is a free and open source C++ framework that is used to create Artificial Intelligence (AI) agents that play the popular Real Time Strategy (RTS) game Starcraft: Brood War. With BWAPI, players of varying levels can create AI agents to play Brood War, but with certain limits. BWAPI will only reveal visible parts of the game state to AI modules to enable programmers to write competitive non-cheating AIs. BWAPI also denies user input so... -
18
Code Catalog in Python
Algorithms and data structures for review for coding interview
code-catalog-python serves as a grab-bag of small, readable Python examples that illustrate common algorithms, data structures, and utility patterns. Each snippet aims to be self-contained and easy to study, with clear inputs, outputs, and the essential logic on display. The catalog format lets you scan for an example, copy it, and adapt it to your use case without wading through a large framework. It favors clarity over micro-optimizations so learners can grasp the idea before worrying about edge performance. ... -
19
Alevin2
Tool for the evaluation of ALgorithms for Embedding VIrtual Networks
ALgorithms for Embedding VIrtual Networks Software to evaluate Virtual Network Embedding Algorithms. -
20
plot.py
direct data plotting and evaluation
The Plot.py project tries to supply a measurement data visualization and treatment framework being easy to use while keeping the freedom for advanced users to execute additional data treatment algorithms. Plotting is done via gnuplot and the script used to produce the graphs can be exported for later use/changes. Many raw experimental data types (mostly of x-ray and neutron scattering experiments) are supported with more to be added on user request. -
21
DeepTraffic
DeepTraffic is a deep reinforcement learning competition
DeepTraffic is a deep reinforcement learning simulation designed to teach and evaluate autonomous driving algorithms in a dense highway environment. The system presents a simulated multi-lane highway where an AI-controlled vehicle must navigate traffic while maximizing speed and avoiding collisions. Participants design neural network policies that determine the vehicle’s actions, such as accelerating, decelerating, changing lanes, or maintaining speed. The project was created as part of an... -
22
Physhun is a Java framework for building and executing finite state machines in J2SE and J2EE environments. State models are defined as collections of Spring beans, and do not use proprietary modeling languages. The framework is simple and flexible.
-
23
Lihang
Statistical learning methods (2nd edition) [Li Hang]
Lihang is an open-source repository that provides educational notes, mathematical derivations, and code implementations based on the book Statistical Learning Methods by Li Hang. The repository aims to help readers understand the theoretical foundations of machine learning algorithms through practical implementations and detailed explanations. It includes notebooks and scripts that demonstrate how key algorithms such as perceptrons, decision trees, logistic regression, support vector... -
24
TEESer
Tool for Engineering Emergent Selforganizing bEhavioR
...Designers can continue to use their domain tools such as favorable simulator and by pluging it into the framework can take advantage of distributed simulations in the grid, various analyzers, optimizers (particle swarm optimization, genetic algorithms, ...). See Wiki pages for more information. -
25
Oryx
Lambda architecture on Apache Spark, Apache Kafka for real-time
Oryx 2 is a realization of the lambda architecture built on Apache Spark and Apache Kafka, but with specialization for real-time large-scale machine learning. It is a framework for building applications but also includes packaged, end-to-end applications for collaborative filtering, classification, regression and clustering. The application is written in Java, using Apache Spark, Hadoop, Tomcat, Kafka, Zookeeper and more. Configuration uses a single Typesafe Config config file, wherein...