Search Results for "train" - Page 6
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Showing 513 open source projects for "train"
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Elephas
Distributed Deep learning with Keras & Spark
... Models are initialized on the driver, then serialized and shipped to workers, alongside with data and broadcasted model parameters. Spark workers deserialize the model, train their chunk of data and send their gradients back to the driver. The "master" model on the driver is updated by an optimizer, which takes gradients either synchronously or asynchronously. Hyper-parameter optimization with elephas is based on hyperas, a convenience wrapper for hyperopt and keras. -
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PyTorch Geometric Temporal
Spatiotemporal Signal Processing with Neural Machine Learning Models
The library consists of various dynamic and temporal geometric deep learning, embedding, and Spatio-temporal regression methods from a variety of published research papers. Moreover, it comes with an easy-to-use dataset loader, train-test splitter and temporal snaphot iterator for dynamic and temporal graphs. The framework naturally provides GPU support. It also comes with a number of benchmark datasets from the epidemiological forecasting, sharing economy, energy production and web traffic... -
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Auto-PyTorch
Automatic architecture search and hyperparameter optimization
While early AutoML frameworks focused on optimizing traditional ML pipelines and their hyperparameters, another trend in AutoML is to focus on neural architecture search. To bring the best of these two worlds together, we developed Auto-PyTorch, which jointly and robustly optimizes the network architecture and the training hyperparameters to enable fully automated deep learning (AutoDL). Auto-PyTorch is mainly developed to support tabular data (classification, regression) and time series... -
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Hello AI World
Guide to deploying deep-learning inference networks
Hello AI World is a great way to start using Jetson and experiencing the power of AI. In just a couple of hours, you can have a set of deep learning inference demos up and running for realtime image classification and object detection on your Jetson Developer Kit with JetPack SDK and NVIDIA TensorRT. The tutorial focuses on networks related to computer vision, and includes the use of live cameras. You’ll also get to code your own easy-to-follow recognition program in Python or C++, and train... -
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FaceRecognitionDotNet
The world's simplest facial recognition api for .NET on Windows, MacOS
The world's simplest facial recognition API for .NET on Windows, MacOS, and Linux. -
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LaMDA-pytorch
Open-source pre-training implementation of Google's LaMDA in PyTorch
Open-source pre-training implementation of Google's LaMDA research paper in PyTorch. The totally not sentient AI. This repository will cover the 2B parameter implementation of the pre-training architecture as that is likely what most can afford to train. You can review Google's latest blog post from 2022 which details LaMDA here. You can also view their previous blog post from 2021 on the model. -
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MathJax
Beautiful and accessible math in all browsers
A JavaScript display engine for mathematics that works in all browsers. No more setup for readers. It just works. MathJax provides tools to transform your content from traditional print sources into modern, accessible web content and ePubs. The MathJax team is available to train your staff in using our resources for preparing online teaching material and creating accessible STEM content. MathJax is highly flexible and can be tailored to the needs of your institution by creating customized... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated... -
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VoiceSmith
[WIP] VoiceSmith makes training text to speech models easy
VoiceSmith makes it possible to train and infer on both single and multispeaker models without any coding experience. It fine-tunes a pretty solid text to speech pipeline based on a modified version of DelightfulTTS and UnivNet on your dataset. Both models were pretrained on a proprietary 5000 speaker dataset. It also provides some tools for dataset preprocessing like automatic text normalization. Windows (only CPU supported currently) or any Linux based operating system. If you want to run... -
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Train-Bit
BBC Micro:Bit based wireless model railway controlller
A simple but effective wireless hand held controller for model railways that use normal DC power, based on two Micro:Bit's and a standard motor driver board. -
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Tux Paint
An award-winning drawing program for children of all ages
... of "magic tools" that provide filter effects (like blur, tint and waves) and interesting drawing tools (like train tracks, bubbles and grass). Tux Paint includes a collection of "starter" images, both coloring-book style and photo-realistic, and a large collections of stamps are available as a separate download. Additional content can be added by parents & teachers, or downloaded from 3rd parties. Tux Paint is available for Windows, macOS, Android, Linux, & Haiku operating systems, among others. -
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ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
ModelFox makes it easy to train, deploy, and monitor machine learning models. Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. Train a machine... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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ml5.js
Friendly machine learning for the web
A neighborly approach to creating and exploring artificial intelligence in the browser. ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies. -
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YOLOv3
Object detection architectures and models pretrained on the COCO data
Fast, precise and easy to train, YOLOv5 has a long and successful history of real time object detection. Treat YOLOv5 as a university where you'll feed your model information for it to learn from and grow into one integrated tool. You can get started with less than 6 lines of code. with YOLOv5 and its Pytorch implementation. Have a go using our API by uploading your own image and watch as YOLOv5 identifies objects using our pretrained models. Start training your model without being an expert... -
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TCNOpen
Components for IEC61375 standardised communication
TCNOpen is an open source initiative which the partner railway industries created with the aim to build in collaboration some key parts of new or upcoming railway standards, commonly known under the name TCN. TCN (Train Communication Network) is a series of international standards (IEC61375) developed by Working Group 43 of the IEC (International Electrotechnical Commission), specifying a communication system for the data communication within and between vehicles of a train. It is currently... -
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ChessForge
Chess analysis, training and testing for Windows.
Chess Forge is a Free and Open Source Windows desktop application facilitating chess training. Use Chess Forge to build your opening repertoire, study endgames, strategy and tactics. Utilize the two distinct solving modes and also Chess Forge's unique Training Mode where you get tested against the workbook's content with the help of the engine. Web Site : https://mindrose.com.au Source Code: https://github.com/czbar/ChessForge Installation info:... -
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English: Cover AO is an FREE 2D MMORPG Open Source based of Argentum Online (https://sourceforge.net/projects/morgoao/) Inside this fantasy free world, set in a medieval era. You are in charge of writing your own story. Where you can build, train, trade, conquer and interact in real time with the rest of the players in the world. Important: Client and server are distributed under Affero GPL v1. - Spanish: Cover AO es un MMORPG 2D GRATUITO de código abierto basado en Argentum...
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Chess-gambiteer
Chess training program playing gambits
Program to train against gambit play (*) with the computer, with a strength adjustable chess engine (Danasah, currently set at approx 1400 EL0 when playing against it). Also included now are two options to watch the engine (with gambit book) play against another engine (either clublevel or IM level). The engine gambit books are made in such a way that 1) real lousy gambits are not played, and 2) the book is choosing a gambit line as often as possible, so for Black it might go for e.g... -
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BARS_checkers
BARS - a program for playing checkers
BARS is a program for playing checkers. In this program, you can play with a robot of different difficulty levels, train your memory, solve tasks, memorize openings, save games played. Enjoy using it :) source code: https://github.com/Ni3nayka/BARS-international VK-group: https://vk.com/bars_checkers if you have a problem starting the program, try: 1) update net.framework 2) update VS MS C++ WARNING: the program works only with windows x64 -
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Simple Russian Typing Trainer
A simple program to train to type in Russian.
A simple trainer for those who already can type with ten fingers and want to learn to type in Russian language on their own keyboards. You do not need a real russian keyboard on your computer. See how-to-use-file. -
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EZAPIYA TYPING MASTER
Ezapiya Typing Master is a software to learn typing, it supports multi
Why Use Ezapiya Typing Master? Support multiple language Ezapiya Typing master support multiple language like English, Hindi and Marathi. 15 hours of interactive exercises The aim of this feature is to guide users and allow them to practice at various levels. The customized exercises ensure that the learning is done based on practice and not just theory. Train difficult words or practice capitals and tricky keys. Typing Master will also suggest certain exercises based on the stats... -
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Program suite for model railroaders, mainly to support operation on modular railroad setups, features model time clock, computer based train dispatching and more
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Proteus Model Builder
GUI for training of neural network models for GuitarML Proteus
... networks. So, Keith Bloemer a.k.a. GuitarML set up a Google Colab script to give people the Opportunity to train their own models online. Still, I thought that things could be easier, and I wanted a faster way to work with the python scripts. So I automated some things on my Windows 10 machine. I assume, that most musicians use this OS. This repository contains a bunch of Windows batch scripts and a freepascal based GUI solution that controls everything from a single window! -
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NAM-Runner
Batch file to install and run NAM (neural-amp-modeler) easily.
A Windows 10 batch file, that installs and runs the NAM model trainer (neural-amp-modeler) by Steven Atkinson right into the GUI application. Fully automated. Custom one-time installation of everything you need to train neural network models of guitar amps and more for the NAM VST plugin, no Conda required. Runs as a launcher afterwards. Portable installation. New pyTorch inclues CUDA runtime for fast Nvidia GPU support. No command line, python or conda knowledge needed! Just double click.