Editor

A NeoVim plugin for highlighting visual selections like in a normal document editor

HighStr.nvim HighStr.nvim is a NeoVim plugin written in Lua that aims to provide a the simple functionality of highlighting text like one would do in a normal document editor. To use it, install it with your favorite plugin manager, select something in visual mode and then run :HSHighlight. You can also add keybindings to the commands. Highlight visual selection in any given pre-defined color. Remove highlighting from lines in visual selection. Users can set up foreground and background of any […]

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Deep Learning

Introducing Convolutions to Vision Transformers

CvT This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolutional vision Transformers (CvT), that improves Vision Transformers (ViT) in performance and efficienty by introducing convolutions into ViT to yield the best of both disignes. This is accomplished through two primary modifications: a hierarchy of Transformers containing a new convolutional token embedding, and a convolutional Transformer block leveraging a convolutional projection. These changes introduce desirable properties of convolutional neural networks (CNNs) […]

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Search

An AI-based image search engine that includes deep transfer learning features Extraction

Deep Image Search Deep Image Search is an AI-based image search engine that includes deep transfer learning features Extraction and tree-based vectorized search technique. Features Faster Search O(logN) Complexity. High Accurate Output Result. Best for Implementing on python based web application or APIs. Best implementation for College students and freshers for project creation. Applications are Images based E-commerce recommendation, Social media and other image-based platforms that want to implement image recommendation and search. Installation This library is compatible with both […]

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Deep Learning

Break down your CNN and visualize the features from within the model

Rover Reverse engineer your CNNs, in style. Rover will help you break down your CNN and visualize the features from within the model. No need to write weirdly abstract code to visualize your model’s features anymore. :computer: Usage git clone https://github.com/Mayukhdeb/rover.git; cd rover install requirements: pip install -r requirements.txt from rover import core from rover.default_models import models_dict core.run(models_dict = models_dict) and then run the script with streamlit as: $ streamlit run your_script.py if everything goes right, you’ll see something like: […]

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Machine Learning

A PyTorch library and evaluation platform for end-to-end compression research

CompressAI CompressAI (compress-ay) is a PyTorch library and evaluation platform for end-to-end compression research. CompressAI currently provides: custom operations, layers and models for deep learning based data compression a partial port of the official TensorFlow compression library pre-trained end-to-end compression models for learned image compression evaluation scripts to compare learned models against classical image/videocompression codecs Note: Multi-GPU support is now experimental. Installation CompressAI supports python 3.6+ and PyTorch 1.4+. pip: pip install compressai Note: wheels are available for Linux and […]

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Advanced, Healthcare, NLP

Will MLOps change the future of the healthcare system?

This article was published as a part of the Data Science Blogathon Overview: From the advent of modern Science & Technology, Researchers are trying to find a far better solution for the real-time problems we face in our day-to-day life. Technologies like Machine learning, AI, deep learning, and NLP give dynastic and diplomatic solutions in various growing sectors like finance, healthcare, and therefore the retail industry, etc, and making it more and more reliable in production. Will Machine Learning change the […]

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Python

Context Managers and Python’s with Statement

The with statement in Python is a quite useful tool for properly managing external resources in your programs. It allows you to take advantage of existing context managers to automatically handle the setup and teardown phases whenever you’re dealing with external resources or with operations that require those phases. Besides, the context management protocol allows you to create your own context managers so you can customize the way you deal with system resources. So, what’s the with statement good for? […]

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Advanced, CHATBOT, conversational AI, NLP

Beginner’s guide before building a Chatbot

This article was published as a part of the Data Science Blogathon According to Accenture – “57% of the Businesses agree that chatbots deliver larger ROI with minimal effort.” Table of Contents : 1. What’s a chatbot? 2. A dive into types of chatbots 3.What are the top platforms to build a chatbot? 4. What are the top Frameworks for building a chatbot? 5. The Algorithm to build a Chatbot. 6. Tips to follow before building your first chatbot 7. Top […]

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gender bias, NLP

Issue #133 – Evaluating Gender Bias in MT

02 Jun21 Issue #133 – Evaluating Gender Bias in MT in Evaluation, The Neural MT Weekly Author: Akshai Ramesh, Machine Translation Scientist @ Iconic Introduction We often tend to personify aspects of life that may vary based upon the beholder’s interpretation. There are plenty of examples for this – “Mother Earth”, Doctor (Men), Cricketer (Men), Nurse(Woman), Cook(Woman), etc. The MT systems are trained with a large amount of parallel corpus which encodes this social bias. If that is the case, […]

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Data Analysis

A fine-grained manually annotated named entity recognition dataset

Few-NERD Few-NERD is a large-scale, fine-grained manually annotated named entity recognition dataset, which contains 8 coarse-grained types, 66 fine-grained types, 188,200 sentences, 491,711 entities and 4,601,223 tokens. Three benchmark tasks are built, one is supervised: Few-NERD (SUP) and the other two are few-shot: Few-NERD (INTRA) and Few-NERD (INTER). The schema of Few-NERD is: Few-NERD is manually annotated based on the context, for example, in the sentence “London is the fifth album by the British rock band…“, the named entity London […]

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