How to Set Up a Django Project

When you start building any new Django web application, there’s a basic setup you need to tackle first. This course outlines the necessary steps to set up a Django project. During this course, you’ll focus on the initial steps you’ll need to take to start a new web application. You should first have Python installed and you should know how to work with virtual environments and Python’s package manager, pip. You won’t need much programming knowledge to follow along with […]

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Shrinking your Python application’s Docker image: an overview

You’ve finished building the initial Docker image for your Python application, you push it to the registry–and that takes a while, because your image is 2GB. Your image is clearly too large, and so your next step is to try to make your Docker image smaller. In this article you’ll find an overview of the many techniques you can use to shrink your image, organized approximately by logical order packaging. The focus is on Python, though many of these techniques […]

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PyTorch implementation of some learning rate schedulers for deep learning researcher

pytorch-lr-scheduler PyTorch implementation of some learning rate schedulers for deep learning researcher. Usage WarmupReduceLROnPlateauScheduler import torch from lr_scheduler.warmup_reduce_lr_on_plateau_scheduler import WarmupReduceLROnPlateauScheduler if __name__ == ‘__main__’: max_epochs, steps_in_epoch = 10, 10000 model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))] optimizer = torch.optim.Adam(model, 1e-10) scheduler = WarmupReduceLROnPlateauScheduler( optimizer, init_lr=1e-10, peak_lr=1e-4, warmup_steps=30000, patience=1, factor=0.3, ) for epoch in range(max_epochs): for timestep in range(steps_in_epoch): … … if timestep < warmup_steps: scheduler.step() val_loss = validate() scheduler.step(val_loss) TransformerLRScheduler import torch from lr_scheduler.transformer_lr_scheduler import TransformerLRScheduler if __name__ == '__main__': max_epochs, steps_in_epoch [...]

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A lighweigt screen color picker tool with python

A lighweigt screen color picker tool. Availability Only GNU/Linux 🐧 Installing Install via pip (No auto-update): [sudo] pip install tkpick Usage $ tkpick or Tkpick or Application Menu > Graphics > Tkpick Shortcuts Shortcut Function Shift-c Copy color to clipboard Shift-a Open the about window Shift-q Close tool GitHub https://github.com/beucismis/tkpick    

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Libraries for creating and controlling interactive web pages with Python

IDOM Libraries for creating and controlling interactive web pages with Python 3.7 and above. Try it Now Click the badge above to get started! It will take you to a Jupyter Notebookshosted by Binder with some great examples. Or Install it Now pip install idom[stable] IDOM can be used to create a simple slideshow which changes whenever a user clicks an image. import idom @idom.component def Slideshow(): index, set_index = idom.hooks.use_state(0) url = f”https://picsum.photos/800/300?image={index}” return idom.html.img({“src”: url, “onClick”: lambda event: […]

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Implementation of Artificial Neural Network Algorithm

Artificial Neural Network This repository contain implementation of Artificial Neural Network Algorithm in several programming languanges and frameworks. This repository was created to introduce artifical neural network in a simple way. Artificial Neural Network has become popular at this time. This branch field of Artificial Intelligence has given powerful impact to our daily life such as social media recomendation system, face detection of camera on phone and many more. Therefore, to keep up our knowledge in technology especially Artificial Intelligence, […]

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A simple weather tool made with python

wwy A simple weather tool made with python, API, and PyPi packaging. Installation # Unix-like pip3 install wwy # Windows pip install wwy Usage # wwy wwy tokyo # wwy wwy tokyo,jp # wwy -u wwy tokyo -u imperial Update # Unix-like pip3 install –upgrade wwy # Windows pip install –upgrade wwy Credits GitHub https://github.com/clieg/wwy    

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The Python Toolbox for Neurophysiological Signal Processing

NeuroKit The Python Toolbox for Neurophysiological Signal Processing This package is the continuation of NeuroKit 1. It’s a user-friendly package providing easy access to advanced biosignal processing routines. Researchers and clinicians without extensive knowledge of programming or biomedical signal processing can analyze physiological data with only two lines of code. Quick Example import neurokit2 as nk # Download example data data = nk.data(“bio_eventrelated_100hz”) # Preprocess the data (filter, find peaks, etc.) processed_data, info = nk.bio_process(ecg=data[“ECG”], rsp=data[“RSP”], eda=data[“EDA”], sampling_rate=100) # Compute […]

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A Python package for Bayesian time series forecasting and inference

orbit A Python package for Bayesian forecasting with object-oriented design and probabilistic models under the hood. This project is stable and being incubated for long-term support. It may contain new experimental code, for which APIs are subject to change. requires PyStan as a system dependency. PyStan is licensed under GPLv3, which is a free, copyleft license for software. Orbit is a Python package for Bayesian time series forecasting and inference. It provides afamiliar and intuitive initialize-fit-predict interface for time series […]

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A Python tool for the generation of geological data of minerals

GebPy Open-source, Python-based tool for the synthetic generation of geological data with focus on minerals, rocks and stratigraphy. developed by Maximilian A. Beeskow GebPy is an open-source, Python-based tool for the synthetic generation of geological data with focus on minerals, rocks and stratigraphy. The main assumption of GepPy is that all rock properties are determined by the mineral assemblage besides structural features. GebPy can be used for educational purposes, for example by the generation of mineralogical data that could be […]

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