Tool

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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Date and Time

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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Geolocation

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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Weather

Help you find the weather of your favorite cities with python

Weather-App A service made with Flask and Python to help you find the weather of your favorite cities. Features Backend using Flask and Jinja Weather API to get the current weather data from all over the world Database to store city names and pictures with SQLite and SQLAlchemy Automatic web scrapping to find pictures of the cities Simple error messages Interactive page made with HTML, CSS, JavaScript and JQuery GitHub https://github.com/cau777/Weather-App    

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Web Crawling & Web Scraping

A tool to easily scrape youtube data using the Google API

YouTube data scraper To easily scrape any data from the youtube homepage, a youtube channel/user, search results, playlists, and a single video itself. Requires Python 3.6+ Installing $ pip3 install -r requirements.txt $ python3 Web-Youtube.py -h Works with: YouTube Homepage, youtube search, channel/user, video, and playlists Usage: Web-Youtube.py [OPTIONS] –link YouTube link –api Google/YouTube API key –comments Get comments from YouTube videos [turning on will increase program run time] –subtitles Get subtitles from YouTube videos –durationseconds Get seconds from YouTube […]

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Python

Build a Blog Using Django, Vue, and GraphQL

Are you a regular Django user? Do you find yourself wanting to decouple your back end and front end, handling data persistence in the API while displaying the data in a client-side framework like React or Vue displays that data, in a single-page app (SPA) in the browser? You’re in luck. This tutorial will take you through the process of building a Django blog back end and a Vue front end, using GraphQL to communicate between them. Projects are an […]

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Beginner, Libraries, NLP, Python, Text, Unstructured Data

Top 8 Python Libraries For Natural Language Processing (NLP) in 2021

This article was published as a part of the Data Science Blogathon. Introduction Natural language processing (NLP) is a field situated at the convergence of data science and Artificial Intelligence (AI) that – when reduced to the basics – is all about teaching machines how to comprehend human dialects and extract significance from the text. This is additionally why Artificial Intelligence is regularly essential for NLP projects. So what’s the reason, why many companies care about NLP? Basically in light […]

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Advanced, Artificial intelligence, Deep Learning, NLP, PyTorch

BERT for Natural Language Inference simplified in Pytorch!

This article was published as a part of the Data Science Blogathon Introduction to BERT: BERT stands for Bidirectional Encoder Representations from Transformers. It was introduced in 2018 by Google Researchers. BERT achieved state-of-art performance in most of the NLP tasks at that time and drawn the attention of the data science community worldwide. It is extensively used today by data science practitioners for various NLP tasks. Details about the working of the BERT model can be found here. Introduction to […]

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Facebook NLP Research
NLP

A Time-Domain Convolutional Recurrent Network for Packet Loss Concealment

Sewon Min, Jordan Boyd-Graber, Chris Alberti, Danqi Chen, Eunsol Choi, Michael Collins, Kelvin Guu, Hannaneh Hajishirzi, Kenton Lee, Jennimaria Palomaki, Colin Raffel, Adam Roberts, Tom Kwiatkowski, Patrick Lewis, Yuxiang Wu, Heinrich Küttler, Linqing Liu, Pasquale Minervini, Pontus Stenetorp, Sebastian Riedel, Sohee Yang, Minjoon Seo, Gautier Izacard, Fabio Petroni, Lucas Hosseini, Nicola De Cao, Edouard Grave, Ikuya Yamada, Sonse Shimaoka, Masatoshi Suzuki, Shumpei Miyawaki, Shun Sato, Ryo Takahashi, Jun Suzuki, Martin Fajcik, Martin Docekal, Karel Ondrej, Pavel Smrz, Hao Cheng, Yelong […]

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Machine Learning

An all-MLP replacement for Transformers in Pytorch

gMLP – Pytorch Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch Install $ pip install g-mlp-pytorch Usage For masked language modelling import torch from g_mlp_pytorch import gMLP model = gMLP( num_tokens = 20000, dim = 512, depth = 6, seq_len = 256 ) x = torch.randint(0, 20000, (1, 256)) logits = model(x) # (1, 256, 20000) For image classification import torch from g_mlp_pytorch import gMLPVision model = gMLPVision( image_size = 256, patch_size = 16, num_classes = 1000, dim […]

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