February 24, 2021 Artificial intelligence Leave a comment

AAAI 2021: Accelerating the impact of artificial intelligence

The purpose of the Association for the Advancement of Artificial Intelligence, according to its bylaws, is twofold. The first is to promote research in the area of AI, and the second is to promote the responsible use of these types of technology. The result was a 35th AAAI Conference on Artificial Intelligence (AAAI-21) schedule that broadens the possibilities of AI and is heavily reflective of a pivotal time in AI research when experts are asking bigger questions about how best to responsibly develop, deploy, and integrate the technology. Microsoft and its researchers have been pursuing

February 24, 2021 Beginner, Career, Computer Vision, Data Exploration, Data Visualization, Deep Learning, Education, Excel, Interviews, Learning Path, NLP, Python, SQL, Statistics, Tableau, TIME SERIES Leave a comment

A Comprehensive Step-by-Step Guide to Become an Industry Ready Data Science Professional

Introduction to Artificial Intelligence and Machine Learning Artificial Intelligence (AI) and its sub-field Machine Learning (ML) have taken the world by storm. From face recognition cameras, smart personal assistants to self-driven cars. We are moving towards a world enhanced by these recent upcoming technologies. It’s the most exciting time to be in this career field! The global Artificial Intelligence market is expected to grow to $400 billion by the year 2025. From Startups to big organizations, all want to join […]

February 24, 2021 Machine Learning

Prediction Intervals for Deep Learning Neural Networks

Prediction intervals provide a measure of uncertainty for predictions on regression problems. For example, a 95% prediction interval indicates that 95 out of 100 times, the true value will fall between the lower and upper values of the range. This is different from a simple point prediction that might represent the center of the uncertainty interval. There are no standard techniques for calculating a prediction interval for deep learning neural networks on regression predictive modeling problems. Nevertheless, a quick and […]

February 24, 2021 Machine Learning

Sensitivity Analysis of Dataset Size vs. Model Performance

Machine learning model performance often improves with dataset size for predictive modeling. This depends on the specific datasets and on the choice of model, although it often means that using more data can result in better performance and that discoveries made using smaller datasets to estimate model performance often scale to using larger datasets. The problem is the relationship is unknown for a given dataset and model, and may not exist for some datasets and models. Additionally, if such a […]

February 22, 2021 Python

Guide to Flask-MongoEngine in Python

Introduction Building a web app almost always means dealing with data from a database. There are various databases to choose from, depending on your preference. In this guide, we shall be taking a look at how to integrate one of the most popular NoSQL databases – MongoDB – with the Flask micro-framework. In this guide, we’ll be exploring how to integrate MongoDB with Flask using a popular library – MongoEngine, and more specifically, its wrapper – Flask-MongoEngine. Alternatively, you can […]

February 22, 2021 NLP

Online Versus Offline NMT Quality: An In-depth Analysis on English–German and German–English

December 8, 2020 By: Maha Elbayad, Michael Ustaszewski, Emmanuelle Esperança-Rodier, Francis Brunet Manquat, Jakob Verbeek, Laurent Besacier Abstract We conduct in this work an evaluation study comparing offline and online neural machine translation architectures. Two sequence-to-sequence models: convolutional Pervasive Attention (Elbayad et al., 2018) and attention-based Transformer (Vaswani et al., 2017) are considered. We investigate, for both architectures, the impact of online decoding constraints on the translation quality through a carefully designed human evaluation on English-German and German-English language pairs, […]

February 22, 2021 Advanced, Classification, NLP, Project, Python, Supervised, Text, Unstructured Data Leave a comment

Sentiment Analysis: VADER or TextBlob?

This article was published as a part of the Data Science Blogathon. What Is Sentiment Analysis? Conclusions are integral to practically all human exercises and are key influencers of our practices. Our convictions and impression of the real world, and the decisions we make, are, to an impressive degree, molded upon how others see and assess the world. Therefore, when we have to settle on a choice, we regularly search out the assessments of others. Opinions and their related concepts […]

February 22, 2021 Advanced, Classification, Deep Learning, NLP, Project, Python, Social Media, Supervised, Technique, Text, Unstructured Data Leave a comment

Sentiment Analysis: Predicting Sentiment Of COVID-19 Tweets

This article was published as a part of the Data Science Blogathon. Introduction Hi folks, I hope you are doing well in these difficult times! We all are going through the unprecedented time of the Corona Virus pandemic. Some people lost their lives, but many of us successfully defeated this new strain i.e. Covid-19. The virus was declared a pandemic by World Health Organization on 11th March 2020. This article will analyze various types of “Tweets” gathered during pandemic times. […]

February 21, 2021 Neural Machine Translation (NMT), NMT Leave a comment

Machine Translation Weekly 69: One-Short learning in MT

This week I will discuss a paper about the one-shot vocabulary learning abilities of machine translation. The title of the paper is Continuous Learning in Neural Machine Translation using Bilingual Dictionaries and will be presented at EACL in May this year. A very similar idea is also presented in a paper Facilitating Terminology Translation with Target Lemma Annotations that will be presented at the same conference. One-shot learning is the ability to learn from a single example. In the context […]

February 21, 2021 Machine Learning

Code Adam Optimization Algorithm From Scratch

Last Updated on February 21, 2021 Gradient descent is an optimization algorithm that follows the negative gradient of an objective function in order to locate the minimum of the function. A limitation of gradient descent is that a single step size (learning rate) is used for all input variables. Extensions to gradient descent like AdaGrad and RMSProp update the algorithm to use a separate step size for each input variable but may result in a step size that rapidly decreases […]

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