Deep Learning, NLP, NMT, AI, ML
optional arguments: -h, –help show this help message and exit –gpu_device GPU_DEVICE Select specific GPU to run the model –batch-size N Input batch size for training (default: 64) –epochs N Number of epochs to train (default: 20) –num-class N Number of classes to classify (default: 10) –lr LR Learning rate (default: 0.01) –weight-decay WD Weight decay (default: 1e-5) –model-path PATH Path to save
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Overview This is a PyTorch implementation of MobileViT specified in “MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer“, arXiv 2021. Usage import torch from mobilevit import mobilevit_xxs net = mobilevit_xxs() img = torch.randn(1, 3, 256, 256) out = net(img) Credits Code adapted from MobileNetV2 and ViT. GitHub https://github.com/chinhsuanwu/mobilevit-pytorch
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Turtle graphics || Python Rainbow (রংধনু) : Rainbow.using.Python.–.Python.Turtle.graphics.mp4 Human robot (মানব রোবট) : Draw.a.human.robot.using.python.–.Turtle.graphics.mp4 Orthocoronavirinae (করোনাভাইরাস ): Draw.Orthocoronavirinae.using.python.mp4 GitHub https://github.com/Jubair-Ahmed-Junjun/turtle
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Just a basic Telegram AI chat bot written in Python using Pyrogram. Requirements Installation $ https://github.com/Rexinazor/Nikko_ChatBot $ cd LunaChatBot $ pip3 install -U -r requirements.txt $ cp sample_config.py config.py Edit config.py with your own values. Heroku Docker Follow Installation Guide. $ sudo docker build -t luna . $ sudo docker run luna GitHub
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Features: Support scraping the courses in MEM to take the vocabulary Support scraping IPA of English Language (US and UK) Support translate to your mother language Appplication Requires Install DB Browser : SQLite Install Library: Window python -m pip install memrise Linux macOS Guidelines How to take Course ID? Access the Website: Memrise and copy the Course ID as the following picture: Import library and initialize database
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Generate report for the given bibtex file. For use: python -m venv venv source venv/bin/activate pip install –upgrade pip pip install https://github.com/igordejanovic/textx-bibtex/archive/refs/heads/master.zip pip install https://github.com/igordejanovic/textx-gen-bibtex-report/archive/refs/heads/main.zip For development: git clone [email protected]:igordejanovic/textx-gen-bibtex-report.git cd textx-gen-bibtex-report python -m venv venv source venv/bin/activate pip install –upgrade pip pip install https://github.com/igordejanovic/textx-bibtex/archive/refs/heads/master.zip pip install -e .[dev,test] Note: for zsh escape [] -> pip install -e .[dev,test] To generate a report from file my.bib:
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An API to run evaluations to assess the FAIRness of a resource. Using FastAPI and MongoDB Requirements Docker Docker Compose Poetry if you need to install new Python packages. Node.js (with npm) if you need to do frontend development Edit the assessments Most of the Python code for the API is in https://github.com/MaastrichtU-IDS/fair-enough/tree/main/backend/app All assessments used to run evaluations are python scripts defined in the same folder: https://github.com/MaastrichtU-IDS/fair-enough/tree/main/backend/app/assessments Backend local development Start the stack for development locally with Docker Compose: […]
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This is a Python package, that allows you to acquire your RecNet bearer token with your account credentials! Done via git: python -m pip install git+https://github.com/Jegarde/RecNet-Login.git#egg=recnetlogin The absolute basics. This is basically all you need to know to get started. from recnetlogin import login_to_recnet login = login_to_recnet(“username”, “password”) print(login) A simple example on how the module is used. Another example script is found here.
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Getting Started You can start this app by activating the virtual environment located in venv of the main project directory and running following command in the terminal: python manage.py runserver Details The app is created with django and django rest-framework and it is designed for learning purpose only. It has all Create, Read, Update and Delete operations which arerequired in all big applications. It covers following concepts: Django Django RestFramework Authentication PostGreSQl integration Models and Views Permissions and Authorization The […]
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