Search Results for "artificial intelligence java source code" - Page 3
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Showing 92 open source projects for "artificial intelligence java source code"
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Tabnine
Vim client for TabNine
... assist, you probably already know that it can save you tons of time, easily cutting your keystrokes in half. Powered by sophisticated machine learning models trained on billions of lines of trusted open source code from GitHub, Tabnine is the most advanced AI-powered code completion copilot available today. And like GitHub, it is an essential tool for professional developers. -
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BudgetML
Deploy a ML inference service on a budget in 10 lines of code
Deploy a ML inference service on a budget in less than 10 lines of code. BudgetML is perfect for practitioners who would like to quickly deploy their models to an endpoint, but not waste a lot of time, money, and effort trying to figure out how to do this end-to-end. We built BudgetML because it's hard to find a simple way to get a model in production fast and cheaply. Deploying from scratch involves learning too many different concepts like SSL certificate generation, Docker, REST, Uvicorn... -
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fastNLP
fastNLP: A Modularized and Extensible NLP Framework
fastNLP is a lightweight framework for natural language processing (NLP), the goal is to quickly implement NLP tasks and build complex models. A unified Tabular data container simplifies the data preprocessing process. Built-in Loader and Pipe for multiple datasets, eliminating the need for preprocessing code. Various convenient NLP tools, such as Embedding loading (including ELMo and BERT), intermediate data cache, etc.. Provide a variety of neural network components and recurrence models... -
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MMdnn
Tools to help users inter-operate among deep learning frameworks
..., which means you can train a model with one framework and deploy it with another. During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform. -
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DETR
End-to-end object detection with transformers
PyTorch training code and pretrained models for DETR (DEtection TRansformer). We replace the full complex hand-crafted object detection pipeline with a Transformer, and match Faster R-CNN with a ResNet-50, obtaining 42 AP on COCO using half the computation power (FLOPs) and the same number of parameters. Inference in 50 lines of PyTorch. What it is. Unlike traditional computer vision techniques, DETR approaches object detection as a direct set prediction problem. It consists of a set-based... -
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Tensor2Tensor
Library of deep learning models and datasets
Deep Learning (DL) has enabled the rapid advancement of many useful technologies, such as machine translation, speech recognition and object detection. In the research community, one can find code open-sourced by the authors to help in replicating their results and further advancing deep learning. However, most of these DL systems use unique setups that require significant engineering effort and may only work for a specific problem or architecture, making it hard to run new experiments... -
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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... -
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RecNN
Reinforced Recommendation toolkit built around pytorch 1.7
This is my school project. It focuses on Reinforcement Learning for personalized news recommendation. The main distinction is that it tries to solve online off-policy learning with dynamically generated item embeddings. I want to create a library with SOTA algorithms for reinforcement learning recommendation, providing the level of abstraction you like. -
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PyTorch Natural Language Processing
Basic Utilities for PyTorch Natural Language Processing (NLP)
PyTorch-NLP is a library for Natural Language Processing (NLP) in Python. It’s built with the very latest research in mind, and was designed from day one to support rapid prototyping. PyTorch-NLP comes with pre-trained embeddings, samplers, dataset loaders, metrics, neural network modules and text encoders. It’s open-source software, released under the BSD3 license. With your batch in hand, you can use PyTorch to develop and train your model using gradient descent. For example, check out... -
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MMF
A modular framework for vision & language multimodal research
MMF is a modular framework for vision and language multimodal research from Facebook AI Research. MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. MMF is built on top of PyTorch... -
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captcha_break
Identification codes
This project will use Keras to build a deep convolutional neural network to identify the captcha verification code. It is recommended to use a graphics card to run the project. The following visualization codes are jupyter notebookall done in . If you want to write a python script, you can run it normally with a little modification. Of course, you can also remove these visualization codes. captcha is a library written in python to generate verification codes. It supports image verification... -
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TGAN
Generative adversarial training for generating synthetic tabular data
... interfering with other software installed in the system where TGAN is run. For development, you can use make install-develop instead in order to install all the required dependencies for testing and code listing. In order to be able to sample new synthetic data, TGAN first needs to be fitted to existing data. -
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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 prediction... -
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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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Image classification models for Keras
Keras code and weights files for popular deep learning models
...' argument in model constructor for all image models, weights='msd' for the music tagging model). Weights are automatically downloaded if necessary, and cached locally in ~/.keras/models/. This repository contains code for the following Keras models, VGG16, VGG19, ResNet50, Inception v3, and CRNN for music tagging. -
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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... -
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Tangent
Source-to-source debuggable derivatives in pure Python
Existing libraries implement automatic differentiation by tracing a program's execution (at runtime, like PyTorch) or by staging out a dynamic data-flow graph and then differentiating the graph (ahead-of-time, like TensorFlow). In contrast, Tangent performs ahead-of-time autodiff on the Python source code itself, and produces Python source code as its output. Tangent fills a unique location in the space of machine learning tools. As a result, you can finally read your automatic derivative code... -
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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... -
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TensorFlow World
Simple and ready-to-use tutorials for TensorFlow
This repository aims to provide simple and ready-to-use tutorials for TensorFlow. The explanations are present in the wiki associated with this repository. There are different motivations for this open source project. TensorFlow (as we write this document) is one of / the best deep learning frameworks available. The question that should be asked is why has this repository been created when there are so many other tutorials about TensorFlow available on the web? Deep Learning is in very high... -
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Command Line Parser GetPot
Tool to parse the command line and configuration files.
Powerful command line and configuration file parsing for C++, Python, Ruby and Java (others to come). This tool provides many features, such as separate treatment for options, variables, and flags, unrecognized object detection, prefixes and much more. -
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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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ffnet is a fast and easy-to-use feed-forward neural network training solution for python. Many nice features are implemented: arbitrary network connectivity, automatic data normalization, very efficient training tools, network export to fortran code. Now ffnet has also a GUI called ffnetui.
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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... -
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Neural Libs
Neural network library for developers
This project includes the implementation of a neural network MLP, RBF, SOM and Hopfield networks in several popular programming languages. The project also includes examples of the use of neural networks as function approximation and time series prediction. Includes a special program makes it easy to test neural network based on training data and the optimization of the network. -
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ProximityForest
Efficient Approximate Nearest Neighbors for General Metric Spaces
... and Pattern Recognition, 2012. This source code is provided without warranty and is available under the GPL license. More commercially-friendly licenses may be available. Please contact Stephen O'Hara for license options. Please view the wiki on this site for installation instructions and examples on reproducing the results of the papers.
