Deep Learning, NLP, NMT, AI, ML
This article was published as a part of the Data Science Blogathon Have you ever thought of generating questions from the SRT files of Movies? I don’t know if we can use this but it is pretty exciting when I came to know as a beginner that we can do that. What is SRT? In simple terms, the subtitles you see in Amazon Prime, Netflix, Hotstar, HBO, etc are saved in a text file with (.srt) extension with timestamps. The timestamp […]
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This article was published as a part of the Data Science Blogathon Introduction We all have made our resumes at some point in time. In a resume, we try to include important facts about ourselves like our education, work experience, skills, etc. Let us work on a resume dataset today. The text we put in our resume speaks a lot about us. For example, our education, skills, work experience, and other random information about us are all present in a resume. […]
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June 6, 2021 By: Lingfei Wu, Yu Chen, Heng Ji, Yunyao Li Abstract This tutorial of Deep Learning on Graphs for Natural Language Processing (DLG4NLP) is timely for the computational linguistics community, and covers relevant and interesting topics, including automatic graph construction for NLP, graph representation learning for NLP, various advanced GNN based models (e.g., graph2seq, graph2tree, and graph2graph) for NLP, and the applications of GNNs in various NLP tasks (e.g., machine translation, natural language generation, information extraction and semantic […]
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Weather-Forecasting Python scripts were created to visualize the weather for over 500 cities across the world at varying distances from the equator. To understand weather patterns for forecasting, a series of scatter plots were created. The scatter plots depicted the relationship between Temperature versus Latitude, Humidity versus Latitude, Cloudiness versus Latitiude, and Wind Speed versus Latitude. One of the relationship is shown below: Linear regressions for each relationship were created separating them in Northern and Southern Hemispheres. More than 500 […]
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seaborn-image Seaborn-image is a Python image visualization library based on matplotlib and provides a high-level API to draw attractive and informative images quickly and effectively. It is heavily inspired by seaborn, a high-level visualization library for drawing attractive statistical graphics in Python. Installation For latest release pip install seaborn-image For latest commit pip install git+https://github.com/SarthakJariwala/seaborn-image GitHub https://github.com/SarthakJariwala/seaborn-image
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Metrinome Metrinome is an all-purpose tool for working with code complexity metrics. It can be used as both a REPL and API, and includes: Converters to turn C++, Java, and Python code to Control Flow Graphs Metric calculators for NPath, Cyclomatic, and Path Complexity KLEE utilities to automatically analyze C++ code To get started, follow the guide in the wiki to pull the docker image and jump directly into the REPL. GitHub https://github.com/hmc-alpaqa/metrinome
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Python-PlexAPI Unofficial Python bindings for the Plex API. Our goal is to match all capabilities of the official Plex Web Client. A few of the many features we currently support are: Navigate local or remote shared libraries. Perform library actions such as scan, analyze, empty trash. Remote control and play media on connected clients, including Controlling Sonos speakers Listen in on all Plex Server notifications. Getting a PlexServer Instance There are two types of authentication. If you are running on […]
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maup maup is the geospatial toolkit for redistricting data. The package streamlines the basic workflows that arise when working with blocks, precincts, and districts, such as The project’s priorities are to be efficient by using spatial indices wheneverpossible and to integrate well with the existing ecosystem aroundpandas, geopandas andshapely. The package is distributedunder the MIT License. Installation We recommend installing maup from conda-forgeusing conda: conda install -c conda-forge maup You can get conda by installingMiniconda, a free Pythondistribution made especially […]
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Greenstalk Greenstalk is a small and unopinionated Python client library for communicating with the beanstalkd work queue. The API provided mostly maps one-to-one with commands in the protocol. Getting Started >>> import greenstalk >>> client = greenstalk.Client((‘127.0.0.1’, 11300)) >>> client.put(‘hello’) 1 >>> job = client.reserve() >>> job.id 1 >>> job.body ‘hello’ >>> client.delete(job) >>> client.close()
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PyAbsorp This is a package developed to be use to find the Sound Absorption Coefficient through some implemented models, like Biot-Allard, Johnson-Champoux and others. This project is in the alpha stage. Dependencies PyAbsorp runs under Linux, Windows and MacOS, a Python 3.8.10 installation is needed with the latest Numpy (1.20.3 or higher) , Scipy (1.6.3 or higher).Matplotlib is recommended, but not necessary. Implemented Models Delany-Bazley (with Miki and Allard-Champoux variation) Biot-Allard Johnson-Champoux (with Allard and Lafarge variation) Rayleigh How to […]
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