In recent years, the field of object detection has seen tremendous progress, aided by the advent of deep learning. Object detection is the task of identifying objects in an image and drawing bounding boxes around them, i.e. localizing them. It’s a very important problem in computer vision due its numerous applications from self-driving cars to security and tracking.

Prior approaches of object detection…

The technology revolution promises to deliver a lot of rewards for organizations that take it by the horns and implement it. The spread of the Internet across every facet of society has meant that the traditional approach to watching videos has changed. Research shows that the average American is still fond of watching videos on the traditional television. The interest in watching TV results in around…

Bayesian Nonparametrics is a class of models with a potentially infinite number of parameters. High flexibility and expressive power of this approach enables better data modelling compared to parametric methods.

Bayesian Nonparametrics is used in problems where a dimension of interest grows with data, for example, in problems where the number of features is not fixed but allowed to vary as we observe more…

From being the plots of sci-fi thrillers to being seen as threats by the working populace, Artificial Intelligence (AI) has during the last few years jumped into the headlines as it has become a part of reality.

People are confused - AI is either considered to be a giant leap towards modernization or a leap towards massive levels of unemployment. It…

Who’s this article for:

This blog is intended for enterprise data analysts, line of business users, and data practitioners who work with qualitative and quantitative data in decision-making.

How enterprise currently use data science and business intelligence today

Quantitative analytics based on statistical models predict outcome with data models built from historical datasets using machine-learning algorithms 

• Enterprise managers use…

Will Artificial Intelligence make subject matter experts obsolete?

Imagine you want to build an application that helps to identify wine and cheese pairings. Who will perform best? Applications solely based on machine learning, those ones which are based on experts' knowledge only, or a combination of both?

Most of the machine learning algorithms were…

These days, many organisations have begun to develop their own knowledge graphs. One reason might be to build a solid basis for various machine learning and cognitive computing efforts. For many of those, it remains still unclear where to start. SKOS offers a simple way to start and opens many doors to extend a knowledge graph over time.

1)…

Years ago, it was very time consuming to translate the text from an unknown language. Using simple vocabularies with word-for-word translation was hard for two reasons: 1) the reader had to know the grammar rules and 2) needed to keep in mind all language versions while translating the whole sentence.

Today, many companies use big data to make super relevant recommendations and growth revenue. Among a variety of recommendation algorithms, data scientists need to choose the best one according a business’s limitations and requirements.

To simplify this task, my team has prepared an overview of the main existing recommendation system…

1.Objective

First of all we will see what is R Clustering, then we will see the Applications of Clustering, Clustering by Similarity Aggregation, use of R amap Package, Implementation of Hierarchical Clustering in R and examples of R clustering in various fields.

2. Introduction to Clustering in…

Text Analytics with Python -- A Practical Real-World Approach to Gaining Actionable Insights from your Data

Text analytics can be a bit overwhelming and frustrating at times with the unstructured and noisy nature of textual data and the vast amount of information available. "Text Analytics with Python" published by Apress\Springer, is a book packed with 385 pages of useful information based on techniques, algorithms,…

Introduction

Enterprise business models evolve over time for many reasons. The Internet has been a key factor driving enterprise business model change in recent years. The recent popularity of smartphones has disrupted consumer habits in travel, investment, entertainment, communication, social engagement, dining, shopping and many daily activities. Consequently, enterprises have been forced to change their business models. While some changes are progressive, others are…

Random Forests algorithm has always fascinated me. I like how this algorithm can be easily explained to anyone without much hassle. One quick example, I use very frequently to explain the working of random forests is the way a company has multiple rounds of interview to hire a candidate. Let me elaborate. 

Say, you appeared for the position of Statistical analyst at WalmartLabs. Now like most of the companies, you don't just have one round of interview. You…

Robotics has been and still is an enormous fascination for us humans. Even when we did not have computers, we were fascinated with Mary Shelley's Frankenstein because the concept of creating life out of nothing so resonates with us.

Fast forward to the modern word, making a…

Introduction

The quarterly earnings call is a critical event for publicly traded companies. Each call serves multiple purposes. It is both an important source of information for investors and an opportunity for a company to present a narrative of operational performance, financial health, and strategic vision in their own terms. It’s also an ideal opportunity for executives seeking to manage and optimize outcomes.

The advent of AI makes it plausible for…