Search Results for "virtual-machine" - Page 44
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Showing 1565 open source projects for "virtual-machine"
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1
AIAlpha
Use unsupervised and supervised learning to predict stocks
...The project typically involves collecting market data, transforming financial indicators into machine learning features, and training models to identify patterns that may predict market trends. It also demonstrates how models can be evaluated through backtesting frameworks that simulate how a strategy would perform using historical market conditions. By combining financial analytics with machine learning algorithms, the repository illustrates the process of building data-driven investment strategies. -
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Nebula reporter
The optional reporter container which reads nebula reports from Kafka
Nebula is an open source-distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking effect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe. Ever wandered how your going to push an update... -
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Nebula worker
The worker node manager container which manages nebula nodes
Nebula is a open source distributed Docker orchestrator designed for massive scales (tens of thousands of servers/worker devices), unlike Mesos/Swarm/Kubernetes it has the ability to have workers distributed on high latency connections (such as the internet) yet have the pods(containers) be managed centrally with changes taking affect (almost) immediately, this makes Nebula ideal for managing a vast cluster of servers\devices across the globe, some example use cases are IoT devices, appliances\virtual appliances located at clients data centers, and edge computing. Nebula imposes no limits on the scale of the cluster, each component in it is designed to scale out to allow millions of workers to be managed by it. Designed to connect to devices that are spread around the globe Nebula is tolerant of network connection issues and will resync the device when it reconnects. ... -
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Django Celery
Old Celery integration project for Django
Celery is a simple, flexible, and reliable distributed system to process vast amounts of messages, while providing operations with the tools required to maintain such a system. It’s a task queue with focus on real-time processing, while also supporting task scheduling. Celery has a large and diverse community of users and contributors, you should come join us on IRC or our mailing-list. Celery is Open Source and licensed under the BSD License. A task queue’s input is a unit of work called a... -
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5
BitMask32
A small tool that makes generating bit masks easy.
A visual view of a virtual 32-bit register, where you can toggle each bit by clicking on it. The value of the `register` is updated and it can be copied to the clipboard by copy buttons. The window can be set on top so you can switch focus to your editor without losing the window. -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments... -
7
pytorch-examples
Simple examples to introduce PyTorch
The pytorch-examples project is a collection of concise and practical examples demonstrating how to use PyTorch for machine learning and deep learning tasks. It focuses on clarity and minimalism, providing small, self-contained scripts that illustrate key concepts such as neural network training, optimization, and data handling. The examples cover a range of topics including supervised learning, generative models, and reinforcement learning, making it a valuable resource for both beginners and experienced practitioners. ... -
8
Rainbow
Rainbow: Combining Improvements in Deep Reinforcement Learning
Combining improvements in deep reinforcement learning. Results and pretrained models can be found in the releases. Data-efficient Rainbow can be run using several options (note that the "unbounded" memory is implemented here in practice by manually setting the memory capacity to be the same as the maximum number of timesteps). -
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RoboSat
Semantic segmentation on aerial and satellite imagery
RoboSat is an end-to-end pipeline written in Python 3 for feature extraction from aerial and satellite imagery. Features can be anything visually distinguishable in the imagery for example: buildings, parking lots, roads, or cars. -
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UnsupervisedMT
Phrase-Based & Neural Unsupervised Machine Translation
Unsupervised Machine Translation is a research repository that implements both phrase-based SMT and neural MT approaches for translation without parallel corpora. The neural component supports multiple architectures—seq2seq, biLSTM with attention, and Transformer—and allows extensive parameter sharing across languages to improve data efficiency. Training relies on denoising auto-encoding and back-translation, with on-the-fly, multithreaded generation of synthetic parallel data to continually refresh supervision signals. ... -
11
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... -
12
ChainerCV
ChainerCV: a Library for Deep Learning in Computer Vision
ChainerCV is a collection of tools to train and run neural networks for computer vision tasks using Chainer. In ChainerCV, we define the object detection task as a problem of, given an image, bounding box-based localization and categorization of objects. Bounding boxes in an image are represented as a two-dimensional array of shape (R,4), where R is the number of bounding boxes and the second axis corresponds to the coordinates of bounding boxes. ChainerCV supports dataset loaders, which can... -
13
FID score for PyTorch
Compute FID scores with PyTorch
This is a port of the official implementation of Fréchet Inception Distance to PyTorch. FID is a measure of similarity between two datasets of images. It was shown to correlate well with human judgement of visual quality and is most often used to evaluate the quality of samples of Generative Adversarial Networks. FID is calculated by computing the Fréchet distance between two Gaussians fitted to feature representations of the Inception network. The weights and the model are exactly the same... -
14
CakeChat
CakeChat: Emotional Generative Dialog System
...The code is flexible and allows to condition model's responses by an arbitrary categorical variable. For example, you can train your own persona-based neural conversational model or create an emotional chatting machine. Hierarchical Recurrent Encoder-Decoder (HRED) architecture for handling deep dialog context. Multilayer RNN with GRU cells. The first layer of the utterance-level encoder is always bidirectional. By default, CuDNNGRU implementation is used for ~25% acceleration during inference. Thought vector is fed into decoder on each decoding step. ... -
15
PivotSuite
Network Pivoting Toolkit
...Using this we can reach different subnet hosts from our pentest machine, which was only accessible from the compromised machine. -
16
pipupgrade
Like yarn outdated/upgrade, but for pip
pipupgrade is a command-line utility designed to streamline the management of Python package dependencies. It automates the process of identifying and upgrading outdated packages across various Python environments, including system-wide installations, virtual environments, and project-specific setups. By analyzing semantic versioning, pipupgrade categorizes updates into major, minor, and patch changes, allowing developers to make informed decisions about which packages to upgrade. Additionally, it supports updating requirements.txt and Pipfile files, ensuring that dependency specifications remain current. ... -
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Platformer 2D Godot Game
Test project with a 2D platform game developing in Godot 3.1
Test project with a 2D platform game developing in Godot 3.1, reusable mechanics for: State Machine, basics AI, Android Games. -
18
Active Learning
Framework and examples for active learning with machine learning model
...It includes several established active learning strategies such as uncertainty sampling, k-center greedy selection, and bandit-based methods, while also allowing for custom algorithm implementations. The framework integrates with both classical machine learning models (SVM, logistic regression) and neural networks. -
19
UniCC LALR(1) Parser Generator
Parser generator, targetting C, C++, Python, JavaScript, JSON and XML
UniCC (UNIversal Compiler-Compiler) compiles an augmented grammar definition into a program source code that parses the described grammar. Because UniCC is intended to be target-language independent, it can be configured via template definition files to emit parsers in almost any programming language. UniCC comes with out of the box support for the programming languages C, C++, Python (both 2.x and 3.x) and JavaScript. Parsers can also be generated into JSON and XML. -
20
easy12306
Automatic recognition of 12306 verification code
Automatic recognition of 12306 verification code using machine learning algorithm. Identify never-before-seen pictures. -
21
MUSE
A library for Multilingual Unsupervised or Supervised word Embeddings
...The training and evaluation pipeline is lightweight and fast, so experimenting with different languages or initialization strategies is easy. Beyond dictionary induction, the learned embeddings are often used as building blocks for downstream tasks like classification, retrieval, or machine translation. -
22
dev-setup
macOS development environment setup
dev-setup is a macOS development-environment bootstrap and reference repository for configuring a new developer machine. It combines documented setup steps with dotfiles and shell scripts that automate common system changes. The project covers command-line tools, editors, terminal customization, Homebrew packages, and developer-oriented macOS defaults. Additional scripts prepare Python data-analysis stacks, AWS and big-data tooling, databases, web development, and Android development. ... -
23
automl-gs
Provide an input CSV and a target field to predict, generate a model
Give an input CSV file and a target field you want to predict to automl-gs, and get a trained high-performing machine learning or deep learning model plus native Python code pipelines allowing you to integrate that model into any prediction workflow. No black box: you can see exactly how the data is processed, and how the model is constructed, and you can make tweaks as necessary. automl-gs is an AutoML tool which, unlike Microsoft's NNI, Uber's Ludwig, and TPOT, offers a zero code/model definition interface to getting an optimized model and data transformation pipeline in multiple popular ML/DL frameworks, with minimal Python dependencies (pandas + scikit-learn + your framework of choice). automl-gs is designed for citizen data scientists and engineers without a deep statistical background under the philosophy that you don't need to know any modern data preprocessing and machine learning engineering techniques to create a powerful prediction workflow. -
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pyfolio
Portfolio and risk analytics in Python
pyfolio is a Python library for performance and risk analysis of financial portfolios developed by Quantopian Inc. It works well with the Zipline open source backtesting library. At the core of pyfolio is a so-called tear sheet that consists of various individual plots that provide a comprehensive image of the performance of a trading algorithm. Here's an example of a simple tear sheet analyzing a strategy. Quantopian also offers a fully managed service for professionals that includes... -
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TradingView Chart Data Extractor
Extract price and indicator data from TradingView charts
...Too many indicators or too low a time resolution will increase the data points and potentially overload the free server. Avoid this by hosting/running the script on your local machine or scraping multiple times with fewer indicators and manually combining the CSV afterward. Simply append the URL of a chart/idea published on TradingView to the link below. This is not the URL of a security's chart, but the URL for a user-published chart.
