Search Results for "intelligence" - Page 16
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Showing 874 open source projects for "intelligence"
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Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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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). Specifically, our Rainbow agent implements the three components identified as most important... -
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The Google Cloud Developer's Cheat Sheet
Cheat sheet for Google Cloud developers
Every product in the Google Cloud family described in <=4 words (with liberal use of hyphens and slashes) by the Google Developer Relations Team. This list only includes products that are publicly available. There are several products in pre-release/private-alpha that will not be included until they go public beta or GA. Many of these products have a free tier. There is also a free trial that will enable you try almost everything. API platforms and ecosystems, developer and management tools,... -
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Facets
Visualizations for machine learning datasets
The power of machine learning comes from its ability to learn patterns from large amounts of data. Understanding your data is critical to building a powerful machine learning system. Facets contains two robust visualizations to aid in understanding and analyzing machine learning datasets. Get a sense of the shape of each feature of your dataset using Facets Overview, or explore individual observations using Facets Dive. Explore Facets Overview and Facets Dive on the UCI Census Income... -
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Replica Dataset
High-fidelity indoor 3D dataset for AI simulation and robotics
Replica Dataset is a high-quality 3D dataset of realistic indoor environments designed to advance research in computer vision, robotics, and embodied AI. Developed by Facebook Research (now Meta AI), it features accurate geometric reconstructions, high-resolution and high dynamic range textures, and comprehensive semantic annotations. Each environment contains detailed models of real-world spaces, including rooms, furniture, glass, and mirror surfaces. The dataset also provides semantic and... -
$300 Free Credits to Build on Google CloudStart your next project with $300 in free Google Cloud credit. Spin up VMs, run containers, query petabytes in BigQuery, or build agents with Gemini Enterprise Agent Platform. Once your credits are used, keep building with 20+ always-free tier products including Compute Engine, Cloud Storage, GKE, and Cloud Run functions. No commitment required—just sign up and start building.
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TGAN
Generative adversarial training for generating synthetic tabular data
We are happy to announce that our new model for synthetic data called CTGAN is open-sourced. The new model is simpler and gives better performance on many datasets. TGAN is a tabular data synthesizer. It can generate fully synthetic data from real data. Currently, TGAN can generate numerical columns and categorical columns. TGAN has been developed and runs on Python 3.5, 3.6 and 3.7. Also, although it is not strictly required, the usage of a virtualenv is highly recommended in order to avoid... -
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TenorSpace.js
Neural network 3D visualization framework
TensorSpace is a neural network 3D visualization framework built using TensorFlow.js, Three.js and Tween.js. TensorSpace provides Keras-like APIs to build deep learning layers, load pre-trained models, and generate a 3D visualization in the browser. From TensorSpace, it is intuitive to learn what the model structure is, how the model is trained and how the model predicts the results based on the intermediate information. After preprocessing the model, TensorSpace supports the visualization... -
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JAS-mine
Toolkit for agent-based & dynamic microsimulation modelling
JAS-mine is a Java platform that aims at providing a unique simulation tool for discrete-event simulations, including agent-based and microsimulation models. With the aim to develop large-scale, data-driven models, the main architectural choice of JAS-mine is to use whenever possible standard, open-source tools already available in the software development community. It has built-in utilities for communicating with an underlying relational database. In addition, the platform provides... -
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DaNNet
Deep Artificial Neural Network framework using Armadillo
DaNNet is a C++ deep neural network library using the Armadillo library as a base. It is intended to be a small and easy to use framework with no other dependencies than Armadillo. It uses independent layer-wise optimization giving you full flexibility to train your network. -
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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 users cannot take control of game units while the AI is in play. ... -
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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newLISP for BSDs, LINUX, MacOS X, SunOS and Win32: small, fast 350+ functions, a -C-, MySQL, PostgreSQL, SQLite, ODBC, TCP/IP, UDP, XML, Java interface, string processing, regular expressions , math, financial, statistical functions, Win32 DLL
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OpenDino
Open Source Java platform for Optimization, DoE, and Learning.
OpenDino is an open source Java platform for optimization, design of experiment and learning. It provides a graphical user interface (GUI) and a platform which simplifies integration of new algorithms as "Modules". Implemented Modules Evolutionary Algorithms: - CMA-ES - (1+1)-ES - Differential Evolution Deterministic optimization algorithm: - SIMPLEX Learning: - a simple Artificial Neural Net Optimization problems: - test functions - interface for executing... -
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NN-SVG
Publication-ready NN-architecture schematics
Illustrations of Neural Network architectures are often time-consuming to produce, and machine learning researchers all too often find themselves constructing these diagrams from scratch by hand. NN-SVG is a tool for creating Neural Network (NN) architecture drawings parametrically rather than manually. It also provides the ability to export those drawings to Scalable Vector Graphics (SVG) files, suitable for inclusion in academic papers or web pages. The tool provides the ability to... -
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The MCAPL Project provides a tool for prototyping BDI agent programming languages and model checking programs written in these languages via an interface to the JavaPathfinder model checker.
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Skater
Python library for model interpretation/explanations
Skater is a unified framework to enable Model Interpretation for all forms of the model to help one build an Interpretable machine learning system often needed for real-world use-cases(** we are actively working towards to enabling faithful interpretability for all forms models). It is an open-source python library designed to demystify the learned structures of a black box model both globally(inference on the basis of a complete data set) and locally(inference about an individual... -
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Jadex is a Belief Desire Intention (BDI) reasoning engine that allows for programming intelligent software agents in XML and Java. The resoning engine is very flexible.
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The source code has been migrated and is now hosted on Github: https://github.com/jenetics/jenetics Jenetics is an advanced Genetic Algorithm, Evolutionary Algorithm and Genetic Programming library, respectively, written in modern day Java. It is designed with a clear separation of the several algorithm concepts, e. g. Gene, Chromosome, Genotype, Phenotype, Population and fitness Function. Jenetics allows you to minimize or maximize the given fitness function without tweaking it. In...
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VideoMan Library
C++ library for image acquisition and visualization
Library for capturing video from cameras, 3d sensors, frame-grabbers, video files and image sequences. It can also display multiple images using OpenGL with different layouts. Easy integration with OpenCV, CUDA... Perfect for computer vision. Keywords: video capture, computer vision, machine vision, opencv, opengl, cameras, video input devices, firewire, usb, gige -
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Face Recognition
World's simplest facial recognition api for Python & the command line
Face Recognition is the world's simplest face recognition library. It allows you to recognize and manipulate faces from Python or from the command line using dlib's (a C++ toolkit containing machine learning algorithms and tools) state-of-the-art face recognition built with deep learning. Face Recognition is highly accurate and is able to do a number of things. It can find faces in pictures, manipulate facial features in pictures, identify faces in pictures, and do face recognition on a... -
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NOMAD is a C++ code that implements the MADS algorithm (Mesh Adaptive Direct Search) for difficult blackbox optimization problems. Such problems occur when the functions to optimize are costly computer simulations with no derivatives.
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Madara
Middleware for distributed applications
The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system is the Knowledge and Reasoning Language (KaRL) Engine, which provides a real-time scripting language for nanosecond execution times hooked into a flexible transport layer for distributed reasoning. ... -
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Image classification models for Keras
Keras code and weights files for popular deep learning models
All architectures are compatible with both TensorFlow and Theano, and upon instantiation the models will be built according to the image dimension ordering set in your Keras configuration file at ~/.keras/keras.json. For instance, if you have set image_dim_ordering=tf, then any model loaded from this repository will get built according to the TensorFlow dimension ordering convention, "Width-Height-Depth". Pre-trained weights can be automatically loaded upon instantiation (weights='imagenet'... -
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Edward
A probabilistic programming language in TensorFlow
A library for probabilistic modeling, inference, and criticism. Edward is a Python library for probabilistic modeling, inference, and criticism. It is a testbed for fast experimentation and research with probabilistic models, ranging from classical hierarchical models on small data sets to complex deep probabilistic models on large data sets. Edward fuses three fields, Bayesian statistics and machine learning, deep learning, and probabilistic programming. Edward is built on TensorFlow. It... -
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PyTorch Book
PyTorch tutorials and fun projects including neural talk
This is the corresponding code for the book "The Deep Learning Framework PyTorch: Getting Started and Practical", but it can also be used as a standalone PyTorch Getting Started Guide and Tutorial. The current version of the code is based on pytorch 1.0.1, if you want to use an older version please git checkout v0.4or git checkout v0.3. Legacy code has better python2/python3 compatibility, CPU/GPU compatibility test. The new version of the code has not been fully tested, it has been tested... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu...
