Deep Learning, NLP, NMT, AI, ML
PythonPercolation Percolation simulation using python. Exemple de percolation : Etude statistique sur le pourcentage de remplissage jusqu’à percolation dépendament de la largeur de la matrice (percolation gauche-droite): Percolation 3D : GitHub https://github.com/TonyChouteau/PythonPercolation
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Fylm Fylm is a wonderful automated command line app for organizing your film media. You can pronounce it Film or File ’em, whichever you like! It uses (highly suspect) heuristics to identify film files (or folders), then looks them up on TMDb to get all the correct details. Once that’s over and done with, it’ll rename them according to your OCD standards, and move them. Features Fylm can: Rename messy files and folders and make them pretty, like high.noon.1952.1080p.this.OTHER-JUNK » […]
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This article was published as a part of the Data Science Blogathon BERT is too kind — so this article will be touching on BERT and sequence relationships! Abstract A significant portion of NLP relies on the connection in highly-dimensional spaces. Typically an NLP processing will take any text, prepare it to generate a tremendous vector/array rendering said text — then make certain transformations. It’s a highly-dimensional charm. At an exceptional level, there’s not much extra to it. We require to […]
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MLP-Mixer-CIFAR10 This repository implements MLP-Mixer as proposed in MLP-Mixer: An all-MLP Architecture for Vision. The paper introduces an all MLP (Multi-layer Perceptron) architecture for computer vision tasks. Yannic Kilcher walks through the architecture in this video. Experiments reported in this repository are on CIFAR-10. What’s included? Distributed training with mixed-precision. Visualization of the token-mixing MLP weights. A TensorBoard callback to keep track of the learned linear projections of the image patches. Notebooks Note: These notebooks are runnable on Colab. If […]
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Among Us Dumpy Gif Maker A tool to make dumpy among us GIFS. Requirements: Java Runtime Environment 15 or newer ImageMagick Usage: Make sure to download the jar! Basic usage: Click and open the jar, select the file, and a file called “dumpy.gif” will be made in the same folder as the jar. CLI usage: java -jar Among-Us-Dumpy-Gif-Maker.jar for defaults java -jar Among-Us-Dumpy-Gif-Maker.jar for choosing a line number. Default is 9. java -jar Among-Us-Dumpy-Gif-Maker.jar for choosing a line number AND […]
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Gitter Gitter is a open-source project created to easily uses the Github’s API to get public informations from a Github’s Account. ⚙️ Set-up A quick guide of how to install and use Juumla. 1. Clone the repository with git clone https://github.com/oppsec/Gitter.git 2. Install the python libraries with pip install -r requirements.txt 3. Run Gitter with python or python3 main.py ☁ Pre-requistes 🔨 Contributing A quick guide of how to contribute with the project. 1. Create a fork from Gitter repository. […]
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PyGAD: Genetic Algorithm in Python This project is part of PyGAD which is an open-source Python 3 library for building the genetic algorithm and optimizing machine learning algorithms (Keras & PyTorch). Check documentation of the GeneticAlgorithmPython project in the PyGAD’s documentation: https://pygad.readthedocs.io/en/latest/README_pygad_ReadTheDocs.html PyGAD supports different types of crossover, mutation, and parent selection. PyGAD allows different types of problems to be optimized using the genetic algorithm by customizing the fitness function. The library is under active development and more features are […]
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pySPEDAS pySPEDAS is an implementation of the SPEDAS framework in python. The Space Physics Environment Data Analysis Software (SPEDAS) framework is written in IDL and contains data loading, data analysis and data plotting tools for various scientific missions (NASA, NOAA, etc.) and ground magnetometers. Requirements Python 3.7+ is required. We recommend Anaconda which comes with a suite of packages useful for scientific data analysis. Installation pySPEDAS supports Windows, macOS and Linux. To get started, install the pyspedas package using PyPI: […]
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A simple wrapper around Python Discord Pixels. Requires Python 3.7+ (3.x where x >= 7). Requires pillow and aiohttp from pip. Example import dpypx # Create a client with your token. client = dpypx.Client(‘my-auth-token’) # You can also set a save file to store ratelimit data between reboots. client = dpypx.Client(‘my-auth-token’, ratelimit_save_file=’ratelimits.json’) # Download and save the canvas. canvas = await client.get_canvas() canvas.save(‘canvas.png’) # And access pixels from it. print(canvas[4, 10]) # Or just fetch specific pixels. print(await client.get_pixel(4, 10)) […]
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PyKaldi PyKaldi is a Python scripting layer for the Kaldi speech recognition toolkit. It provides easy-to-use, low-overhead, first-class Python wrappers for the C++ code in Kaldi and OpenFst libraries. You can use PyKaldi to write Python code for things that would otherwise require writing C++ code such as calling low-level Kaldi functions, manipulating Kaldi and OpenFst objects in code or implementing new Kaldi tools. You can think of Kaldi as a large box of legos that you can mix and […]
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