Dataset for eye-tracking tasks

In recent years many different deep neural networks were developed, but due to a large number of layers in deep networks, their training requires a long time and a large number of datasets. Today is popular to use trained deep neural networks for various tasks, even for simple ones in which such deep networks are not required… The well-known deep networks such as YoloV3, SSD, etc. are intended for tracking and monitoring various objects, therefore their weights are heavy and […]

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Utilizing consumer cameras for contact-free physiological measurement in telehealth and beyond

Our research is enabling robust and scalable measurement of physiology. Cameras on everyday devices can be used to detect subtle changes in light reflected from the body caused by physiological processes. Machine learning algorithms are then used to process the camera images and recover the underlying pulse and respiration signals that can then be used for health and wellness tracking. According to the CDC WONDER Online Database, heart disease is currently the leading cause of death for both men and […]

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A Microsoft custom data type for efficient inference

AI is taking on an increasingly important role in many Microsoft products, such as Bing and Office 365. In some cases, it’s being used to power outward-facing features like semantic search in Microsoft Word or intelligent answers in Bing, and deep neural networks (DNNs) are one key to powering these features. One aspect of DNNs is inference—once these networks are trained, they use inference to make judgments about unknown information based on prior learning. In Bing, for example, DNN inference […]

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Step by step guide to extract insights from free text (unstructured data)

Text Mining is one of the most complex analysis in the industry of analytics. The reason for this is that, while doing text mining, we deal with unstructured data. We do not have clearly defined observation and variables (rows and columns). Hence, for doing any kind of analytics, you need to first convert this unstructured data into a structured dataset and then proceed with normal modelling framework. The additional step of converting an unstructured data into a structured format is […]

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Step by step guide to building sentiment analysis model using graphlab

I have been using graph lab for quite some time now. The first Kaggle competition I used it for was Click Trough Rate (CTR) and I was amazed to see the speed at which it can crunch such big data. Over last few months, I have realised much broader applications of GraphLab. In this article I will take up the text mining capability of GraphLab and solve one of the Kaggle problems. I will be referring to this problem with […]

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Natural Language Processing Made Easy – using SpaCy (​in Python)

Introduction Natural Language Processing is one of the principal areas of Artificial Intelligence. NLP plays a critical role in many intelligent applications such as automated chat bots, article summarizers, multi-lingual translation and opinion identification from data. Every industry which exploits NLP to make sense of unstructured text data, not just demands accuracy, but also swiftness in obtaining results. Natural Language Processing is a capacious field, some of the tasks in nlp are – text classification, entity detection, machine translation, question […]

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Building a FAQ Chatbot in Python – The Future of Information Searching

Introduction What do we do when we need any information? Simple: “We Ask, and Google Tells”. But if the answer depends on multiple variables, then the existing Ask-Tell model tends to sputter. State of the art search engines usually cannot handle such requests. We would have to search for information available in bits and pieces and then try to filter and assemble relevant parts together. Sounds time consuming, doesn’t it? Source: Inbenta This Ask-Tell model is evolving rapidly with the […]

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A Comprehensive Guide to Understand and Implement Text Classification in Python

Improving Text Classification Models While the above framework can be applied to a number of text classification problems, but to achieve a good accuracy some improvements can be done in the overall framework. For example, following are some tips to improve the performance of text classification models and this framework. 1. Text Cleaning : text cleaning can help to reducue the noise present in text data in the form of stopwords, punctuations marks, suffix variations etc. This article can help to understand how […]

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Top 5 Machine Learning GitHub Repositories & Reddit Discussions (October 2018)

Introduction “Should I use GitHub for my projects?” – I’m often asked this question by aspiring data scientists. There’s only one answer to this – “Absolutely!”. GitHub is an invaluable platform for data scientists looking to stand out from the crowd. It’s an online resume for displaying your code to recruiters and other fellow professionals. The fact that GitHub hosts open-source projects from the top tech behemoths like Google, Facebook, IBM, NVIDIA, etc. is what adds to the gloss of […]

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Introduction to Flair for NLP: A Simple yet Powerful State-of-the-Art NLP Library

Introduction Last couple of years have been incredible for Natural Language Processing (NLP) as a domain! We have seen multiple breakthroughs – ULMFiT, ELMo, Facebook’s PyText, Google’s BERT, among many others. These have rapidly accelerated the state-of-the-art research in NLP (and language modeling, in particular). We can now predict the next sentence, given a sequence of preceding words. What’s even more important is that machines are now beginning to understand the key element that had eluded them for long. Context! Understanding context […]

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