Search Results for "website using python" - Page 65
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Showing 2995 open source projects for "website using python"
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1
Miasm
Reverse engineering framework in Python
The Miasm intermediate representation is used for multiple task: emulation through its jitter engine, symbolic execution, DSE, program analysis, but the intermediate representation can be a bit hard to read. We will present in this article new tricks Miasm has learned in 2018. Among them, the SSA/Out-of-SSA transformation, expression propagation and high-level operators can be joined to “lift” Miasm IR to a more human-readable language. We use graphviz to illustrate some graphs. Its layout... -
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SVPhylA
SVPhylA: Sequence Vectorization for Phylogenetic Analyses
SVPhylA is a python tool for the calculation of several alignment-free distances for phylogenetics analysis from the most popular alignment-free approaches. Such alignment-free methods basically encode DNA and protein sequences (fasta files) into numerical vectors allowing the calculation of alignment-free distances which may be combined into a consensus/compromise matrix by using algorithms like DISTATIS based on Multidimensional Scaling (MSD), Lineal Principal Component Analysis (PCA) and PCA-Kernel (non-lineal). ... -
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Farduino
A commodity IR receiver is used to make an IR link to an Arduino
By using special coding it's possible to use a cheap IR LED and IR receiver as used in remote controls to make a simple infrared link between Arduinos using the built in serial port. The problem with RS232 is that it contains a DC component. By restricting the character set such that the DC component is eliminated, it's possible to send and receive data using a commodity remote control IR receiver. -
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Flask-GraphQL
Adds GraphQL support to your Flask application
Adds GraphQL support to your Flask application. This will add /graphql endpoint to your app and enable the GraphiQL IDE. If you are using the Schema type of Graphene library, be sure to use the graphql_schema attribute to pass as schema on the GraphQLView view. Otherwise, the GraphQLSchema from graphql-core is the way to go. The GraphQLSchema object that you want the view to execute when it gets a valid request. A value to pass as the context_value to graphql execute function. By default is... -
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5
Website out of the box
Simple, out of the box PHP CMS solution that does not need a database.
PHPWOB is an out of the box PHP solution for websites where a full blown content management system would be overkill, but where a dynamically generated menu would still be desired. No database required. Skinable using a simple template and CSS. -
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jpathpy
Select items from JSON by using JPath syntax
Package ``jpathpy`` is easy way for selecting items from objects, that can be iterate by keys or indices (such as dictionaries or lists). See full doc at <https://github.com/vowatchka/jpathpy>. -
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PsPM
A matlab suite for Psycho-Physiological Modelling
...IMPORTANT: As of 04.11.2019, the official code repository for PsPM has been moved to https://github.com/bachlab/PsPM . In order to check the latest releases, discussion and bug reports, please refer to our new repository. Although this website will maintained to provide further releases, there will be no active development on the SVN repository. Thank you for your understanding and for using PsPM. -
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DisplayCAL
Display Calibration and Characterization powered by Argyll CMS
DisplayCAL (formerly known as dispcalGUI) is a graphical user interface for the display calibration and profiling tools of Argyll CMS, an open source color management system. Calibrate and characterize your display devices using one of the many supported measurement instruments, with support for multi-display setups and a variety of available settings like customizable whitepoint, luminance, tone response curve as well as the option to create accurate look-up-table ICC profiles as well as... -
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GraphicsFuzz
A testing framework for automatically finding and simplifying bugs
GraphicsFuzz is a framework developed by Google for testing and fuzzing graphics drivers using automatically generated GLSL shaders. It helps identify security vulnerabilities, driver crashes, and rendering inconsistencies in OpenGL and Vulkan drivers by feeding them randomized but valid shader programs. Originally developed through academic research, GraphicsFuzz automates the process of minimizing and analyzing problematic shaders, helping hardware vendors and driver developers improve the... -
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10
Alien Evolution
A websocket based realtime browser application using the Alien Cipher.
Alien Evolution realtime socket based delivery. What makes this website truly unique is we are using a Alien Cipher encoded websocket to deliver the html and javascript to the client. After the initial key exchange Alien completely drops http transfers from the mix and pushes the Alien Evolution website to client via the socket. Secure content delivery and realtime interactivity are the primary goals of this project and Alien Evolution does not disappoint. ... -
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reg
Docker registry v2 command line client and repo listing generator
Docker registry v2 command line client and repo listing generator with security checks. We do not allow users to pass all the custom certificate flags on commands because it is unnecessarily messy and can be handled through Linux itself. Which we believe is a better user experience than having to pass three different flags just to communicate with a registry using self-signed or private certificates. -
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gatsby-plugin-intl
Turn your website into an internationalization-framework
Internationalize your Gatsby site. Gatsby plugin that turns your website into an internationalization framework out of the box. When you build multilingual sites, Google recommends using different URLs for each language version of a page rather than using cookies or browser settings to adjust the content language on the page. -
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Scikit-learn Tutorial
An introductory tutorial for scikit-learn
Scikit-learn Tutorial contains the materials for Jake VanderPlas’s introductory scikit-learn tutorial, originally used at major Python conferences. It provides a collection of notebooks that walk attendees from basic machine-learning concepts into practical modeling using the scikit-learn library. The tutorial covers data preparation, model fitting, evaluation, and common algorithms such as classification, regression, clustering, and dimensionality reduction. It is designed for people who already have a working Python environment and some familiarity with NumPy, SciPy, and Matplotlib. ... -
14
Twint
An advanced Twitter scraping & OSINT tool written in Python
Twint is an advanced open-source Twitter scraping and OSINT tool written in Python that extracts tweets, user data, followers, likes, and more—without relying on Twitter’s API—making it highly useful for researchers, analysts, and hobbyists who want to bypass rate limits and access public Twitter data. -
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Django_blog
A blog application made with Django and bootstrap
A blog application made on Django. -
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Jarvis
Personal Assistant for Linux and macOS
...He can tell you the weather, he can find restaurants and other places near you. He can do some great stuff for you. In order to start Jarvis just clone this repository and run python installer. Run Jarvis from anywhere by command jarvis. You can start by typing help within the Jarvis command line to check what Jarvis can do for you. Plugins may be modified using the decorators @alias, @require and @complete. These special decorators may be used in any order or several times.Not all plugins are compatible with every system. ... -
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YouTube-8M
Starter code for working with the YouTube-8M dataset
youtube-8m is Google’s open source starter code and reference implementation for training and evaluating machine learning models on the YouTube-8M dataset, one of the largest video understanding datasets publicly released. The repository provides a complete pipeline for video-level and frame-level modeling using TensorFlow, including data reading, model training, evaluation, and inference. It was developed to support the YouTube-8M Video Understanding Challenge (hosted on Kaggle and featured... -
18
DeepSDF
Learning Continuous Signed Distance Functions for Shape Representation
DeepSDF is a deep learning framework for continuous 3D shape representation using Signed Distance Functions (SDFs), as presented in the CVPR 2019 paper DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation by Park et al. The framework learns a continuous implicit function that maps 3D coordinates to their corresponding signed distances from object surfaces, allowing compact, high-fidelity shape modeling. Unlike traditional discrete voxel grids or meshes, DeepSDF... -
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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. -
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Deep Learning with PyTorch
Latest techniques in deep learning and representation learning
This course concerns the latest techniques in deep learning and representation learning, focusing on supervised and unsupervised deep learning, embedding methods, metric learning, convolutional and recurrent nets, with applications to computer vision, natural language understanding, and speech recognition. The prerequisites include DS-GA 1001 Intro to Data Science or a graduate-level machine learning course. To be able to follow the exercises, you are going to need a laptop with Miniconda (a... -
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Azure Machine Learning Python SDK
Python notebooks with ML and deep learning examples
Azure Machine Learning Python SDK is a curated repository of Python-based Jupyter notebooks that demonstrate how to develop, train, evaluate, and deploy machine learning and deep learning models using the Azure Machine Learning Python SDK. The content spans a wide range of real-world tasks — from foundational quickstarts that teach users how to configure an Azure ML workspace and connect to compute resources, to advanced tutorials on using pipelines, automated machine learning, and dataset handling. ... -
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NiftyNet
An open-source convolutional neural networks platform for research
An open-source convolutional neural networks platform for medical image analysis and image-guided therapy. NiftyNet is a TensorFlow-based open-source convolutional neural networks (CNNs) platform for research in medical image analysis and image-guided therapy. NiftyNet’s modular structure is designed for sharing networks and pre-trained models. Using this modular structure you can get started with established pre-trained networks using built-in tools. Adapt existing networks to your imaging... -
23
nGraph
nGraph has moved to OpenVINO
Frameworks using nGraph Compiler stack to execute workloads have shown up to 45X performance boost when compared to native framework implementations. We've also seen performance boosts running workloads that are not included on the list of Validated workloads, thanks to nGraph's powerful subgraph pattern matching. Additionally, we have integrated nGraph with PlaidML to provide deep learning performance acceleration on Intel, nVidia, & AMD GPUs. nGraph Compiler aims to accelerate developing... -
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MatchZoo
Facilitating the design, comparison and sharing of deep text models
The goal of MatchZoo is to provide a high-quality codebase for deep text matching research, such as document retrieval, question answering, conversational response ranking, and paraphrase identification. With the unified data processing pipeline, simplified model configuration and automatic hyper-parameters tunning features equipped, MatchZoo is flexible and easy to use. Preprocess your input data in three lines of code, keep track parameters to be passed into the model. Make use of MatchZoo... -
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PyTorch-BigGraph
Generate embeddings from large-scale graph-structured data
PyTorch-BigGraph (PBG) is a system for learning embeddings on massive graphs—think billions of nodes and edges—using partitioning and distributed training to keep memory and compute tractable. It shards entities into partitions and buckets edges so that each training pass only touches a small slice of parameters, which drastically reduces peak RAM and enables horizontal scaling across machines. PBG supports multi-relation graphs (knowledge graphs) with relation-specific scoring functions,...
