Email

Heimdall Watchtower: Training Progress Email Notifier

Training-Progress-Email-Notifier Heimdall watchtower automatically sends you emails to notify you of the latest progress of your deep learning programs. In this way, you will know how the training goes wherever you are. You will also know as soon as your program has been terminated by other users šŸ™‚ Usage Quick Start Simply run python email_test.py Please note: The test email is sent through a temporal gmail account. It may reach its capacity for sending emails through the SMTP server. In […]

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Machine Learning

Generates vivid and realistic painting artworks with controllable styles in python

Stylized Neural Painting Official PyTorch implementation of the preprint paper “Stylized Neural Painting”, accepted to CVPR 2021.We propose an image-to-painting translation method that generates vivid and realistic painting artworks with controllable styles. Different from previous image-to-image translation methods that formulate the translation as pixel-wise prediction, we deal with such an artistic creation process in a vectorized environment and produce a sequence of physically meaningful stroke parameters that can be further used for rendering. Since a typical vector render is not […]

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Advanced, NLP, Project, Python, Text, Unstructured Data

Part 2: Step by Step Guide to NLP ā€“ Knowledge Required to Learn NLP

This article was published as a part of theĀ Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). In part-1 of this blog series, we complete the basic concepts of NLP. Now, in continuation of that part, in this article, we will cover some of the new concepts. In this article, we will understand the knowledge required and levels of NLP in a detailed manner. In the last of this article, we […]

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Beginner, Data Cleaning, Machine Learning, NLP, Python, Text, Word Embeddings

Part 5: Step by Step Guide to Master NLP ā€“ Word Embedding and Text Vectorization

This article was published as a part of theĀ Data Science Blogathon Introduction This article is part of an ongoing blog series on Natural Language Processing (NLP). Up to the previous part of this article series, we almost completed the necessary steps involved in text cleaning and normalization pre-processing. After that, we will convert the processed text to numeric feature vectors so that we can feed it to computers for Machine Learning applications. NOTE: Some concepts included in the pipeline of […]

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Advanced, conversational AI, NLP

Memory Networks for Q&A(Question and Answer) Applications

This article was published as a part of theĀ Data Science Blogathon Introduction This article shows the power of Memory Networks for Question and Answer (QA) applications in the context of simple natural language-based reasoning. Table of Contents What is the motivation behind Memory Networks? Why do we need Memory Networks when traditional NLP models are already performing well? Facebook bAbI dataset About Supporting Fact Components of Memory Networks How can we find the best match? How does the dot product […]

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Advanced, Classification, Libraries, NLP, Project, Python, Structured Data, Supervised, Text

Sentiment Analysis using NLTK ā€“ A Practical Approach

This article was published as a part of theĀ Data Science Blogathon Introduction The ultimate goal of this blog is to predict the sentiment of a given text using python where we use NLTK aka Natural Language Processing Toolkit, a package in python made especially for text-based analysis. So with a few lines of code, we can easily predict whether a sentence or a review(used in the blog) is a positive or a negative review. Before moving on to the implementation […]

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Classification, News, NLP, Project, Python, Text

Build Your Own Fake News Classifier With NLP

img src:Ā https://wallpapercave.com/w/wp7461543 Introduction The major objective of watching or reading news was to be informed about whatever is happening around us. There are several social media platforms in the current modern era, like Facebook, Twitter, Reddit, and so forth where millions of users would rely upon for knowing day-to-day happenings. Then came the fake news which spread across people as fast as the real news could. Fake news is a piece of incorporated or falsified information often aimed at misleading […]

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Clustering, NLP, Project, Python, Text, Topic Modeling

Topic Modelling With LDA -A Hands-on Introduction

This article was published as a part of theĀ Data Science Blogathon Introduction Imagine walking into a bookstore to buy a book on world economics and not being able to figure out the section of the store that has this book, assuming the bookstore has simply stacked all types of books together. You then realize how important it is to divide the bookstore into different sections based on the type of book. Topic Modelling is similar to dividing a bookstore based […]

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Tool

VSCode extension to sort and refactor python imports using reorder-python-imports

reorder-python-imports VSCode extension to sort and refactor python imports using reorder-python-imports. Unlike other import organizers, reorder-python-imports focuses on reducing the frequency of merge conflicts, using static analysis to avoid the need for an active venv, and providing better better compatibility with pre-commit and black. To learn more, visit the original tool’s homepage. Features Refactoring the imports is provided as a code action, which can be invoked by right clicking on the code or the lightbulb icon and selecting Reorder Imports, […]

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Text

Extract city and country mentions from Text like GeoText without regex

flashgeotext Extract and count countries and cities (+their synonyms) from text, like GeoText on steroids using FlashText, a Aho-Corasick implementation. Flashgeotext is a fast, batteries-included (and BYOD) and native python library that extracts one or more sets of given city and country names (+ synonyms) from an input text. Usage from flashgeotext.geotext import GeoText geotext = GeoText() input_text = ”’Shanghai. The Chinese Ministry of Finance in Shanghai said that China plans to cut tariffs on $75 billion worth of goods […]

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