June 8, 2021 Machine Learning

Open-Source Toolkit for End-to-End Speech Recognition leveraging PyTorch-Lightning

Openspeech Openspeech provides reference implementations of various ASR modeling papers and three languages recipe to perform tasks on automatic speech recognition. We aim to make ASR technology easier to use for everyone. Openspeech is backed by the two powerful libraries — PyTorch-Lightning and Hydra.Various features are available in the above two libraries, including Multi-GPU and TPU training, Mixed-precision, and hierarchical configuration management. Get Started We use Hydra to control all the training configurations.If you are not familiar with Hydra we […]

June 8, 2021 Graph

An easy-to-use library for R&D at the intersection of Deep Learning on Graphs

Graph4NLP Graph4NLP is an easy-to-use library for R&D at the intersection of Deep Learning on Graphs and Natural Language Processing (i.e., DLG4NLP). It provides both full implementations of state-of-the-art models for data scientists and also flexible interfaces to build customized models for researchers and developers with whole-pipeline support. Built upon highly-optimized runtime libraries including DGL , Graph4NLP has both high running efficiency and great extensibility. The architecture of Graph4NLP is shown in the following figure, where boxes with dashed lines […]

June 8, 2021 Command-line Tools

Command line driven CI frontend and development task automation tool

tox At its core tox provides a convenient way to run arbitrary commands in isolated environments to serve as a single entry point for build, test and release activities. Example: run tests with Python 3.7 and Python 3.8 tox is mainly used as a command line tool and needs a tox.ini or a tool.tox section in pyproject.toml containingthe configuration. To test a simple project that has some tests, here is an example with a tox.ini in the root of the […]

June 8, 2021 Database

A Python DB API and SQLAlchemy dialect for querying non-SQL resources like APIs

shillelagh Shillelagh is a Python DB API and SQLAlchemy dialect for querying non-SQL resources like APIs and files. You can use it to write queries like this: INSERT INTO “csv:///path/to/file.csv” SELECT time, chance_of_rain FROM “https://api.weatherapi.com/v1/history.json?key=XXX&q=London” WHERE strftime(‘%Y-%m-%d’, time) IN ( SELECT day FROM “https://docs.google.com/spreadsheets/d/1_rN3lm0R_bU3NemO0s9pbFkY5LQPcuy1pscv8ZXPtg8/edit#gid=2064361835” ) The query above reads holidays from a Google Sheet, uses the days to get weather data from WeatherAPI, and writes the change of rain at each hour of the holidays into a (pre-existing) CSV file. […]

June 8, 2021 Data Analysis

A Python Framework for Modeling and Analysis of Signaling Systems

BioMASS Mathematical modeling is a powerful method for the analysis of complex biological systems. Although there are many researches devoted on producing models to describe dynamical cellular signaling systems, most of these models are limited and do not cover multiple pathways. Therefore, there is a challenge to combine these models to enable understanding at a larger scale. Nevertheless, larger network means that it gets more difficult to estimate parameters to reproduce dynamic experimental data needed for deeper understanding of a […]

June 8, 2021 Deep Learning

A Python library for secure and private Deep Learning

PySyft PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE)) within the main Deep Learning frameworks like PyTorch and TensorFlow. Most software libraries let you compute over the information you own and see inside of machines you control. However, this means that you cannot compute on information without first obtaining (at least partial) ownership of that information. It also means that you cannot compute using machines […]

June 8, 2021 Python

Python Basics: Setting Up Python

Setting up Python is the first step to becoming a Python programmer. In this course, you’ll learn how to download and install Python for Windows, macOS, and Ubuntu Linux and how to open Python’s Integrated Development and Learning Environment, IDLE. There are many ways to install Python. You can download official Python distributions from Python.org, install from a package manager, and even install specialized distributions for scientific computing, Internet of Things, and embedded systems. This course focuses on official distributions, […]

June 8, 2021 Deep Learning, Intermediate, NLP, Unstructured Data

Generate Questions from Movies!

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 […]

June 8, 2021 Beginner, Data Science, Machine Learning, NLP, Python, Text

Text Analytics of Resume Dataset with NLP!

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. […]

June 7, 2021 NLP

Deep Learning on Graphs for Natural Language Processing

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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