In this article an implementation of the Lucas-Kanade optical flow algorithm is going to be described. This problem appeared as an assignment in  a computer vision course from UCSD. The inputs will be sequences of images (subsequent frames from a video) and the algorithm will output an optical flow field (u, v) and trace the motion of the moving objects. The problem description is taken from the assignment itself.

Problem…

In this article, an implementation of an efficient graph-based image segmentation technique will be described, this algorithm was proposed by Felzenszwalb et. al. from MIT.  The slides on this paper can be found from Stanford Vision Lab.. The algorithm is closely related…

Summary: The Magic Quadrant for Advanced Analytic and ML Platforms is just out and there are some big changes in the leaderboard.  Not only are there some surprising upgrades but some equally notable long falls.

The Gartner Magic Quadrant for Advanced Analytic and ML Platforms came out on February 22^nd and there are some big changes in the leaderboard.  Not only are there some surprising upgrades (Alteryx, KNIME, H20.ai) but some…

What is Blockchain Technology? How one blockchain can have Infinite possibilities & opportunities in hand in this red ocean market world.

Some background

Blockchain as on date is mystery story for many (Including my self). We have heard lot that it yields strong potential in global supply chain. It is become the backbone tracking architecture for an evolving and fully transparent grid. Investors can also look to…

What is Raspberry Pi?

Raspberry Pi is a small computer that costs between $5 and $35 but can function as a desktop computer or be used for additional functions, such as building smart devices. Originally, the Pi was intended for usage in schools as a method of increasing interest in computers among children and as a tool to teach them basic coding.…

If many of your clients don’t understand the difference between artificial intelligence (AI) and intelligent systems, you’re not alone. There’s a deeply rooted misconception about AI that isn’t going to clear up anytime soon.

AI has become a marketing buzzword and is being used interchangeably with computer algorithms that analyze data and produce a…

A Guide for Making Black Box Models Explainable, by Christoph Molnar.

Preface

Machine learning has a huge potential to improve products, processes and research. But machines usually don’t give an explanation for their predictions, which hurts trust and creates a barrier for the adoption of machine learning. This book is about making machine learning models and their decisions interpretable.

Machine learning models are already used to choose the best…

This collection covers much more than the topics listed in the title. It also features Azure, Python, Tensorflow, data visualization, and many other cheat sheets. Additional cheat sheets can be found here and here. Below is a screenshot (extract from the data…

Great infographics on how to choose the right language for you to learn. Originally posted here.…

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc. They all aim to generate specific insights from the data.  In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science.

Foundations of Data Science

Authors: Blum,…

Here is a list of potential projects to help you complete your master in data science or in a related field. 

Project #8: Detecting fake reviews on Amazon

Business and Applied Data Science

1. Clustering 2,000+…

In general, any expression of performance that applies to a department can, if the data system is configured properly, be stated in relation to individual workers.  For instance, if # of sales contracts / # of customer enquiries = success rate, the success rate can be given for the entire dealership and also for each sales agent in that dealership.  Due to the differences in performance between agents, it can be problematic to only make use of the aggregate.  Some agents might be blamed for…

Monday newsletter published by Data Science Central. Previous editions can be found here.  The contribution flagged with a + is our selection for the picture of the week.

Announcements

• The shortage of data analysts in the U.S. represents a major career opportunity for you. Leverage…

Data analytics is a red-hot field in terms of growth and popularity, but there’s a relatively new segment of the field that’s starting to catch fire: Email analytics.

Typically, email analytics have referred to email marketing, including measures such as open rates, click-through rates, and unsubscribe rates. But what about everyday emails that you send to your colleagues, superiors, employees, clients, and vendors?

New tools are starting to emerge for this type of analysis,…

It seems that much of the data analysis work I've done over the last few months has followed a "script". First, identify data, often government-sponsored and freely-available, that's of keen interest. Next, find the websites that house the data and download the relevant files to my notebook. The downloads might ultimately be one-off or included in the data analysis programs. Finally, load the data into either R or python and have at it with queries, visualizations, and…

I recently posted a table summarizing probabilistic properties of digits in various number representation systems, see here.  The topic is already rather difficult for well-behaved systems (those listed in my table) but some systems are rogue, and do not have these nice statistical properties. Here we focus on one of these less known systems,…

Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you.

About This Book

• Step into the amazing world of intelligent apps using this comprehensive guide
• Enter the world of Artificial Intelligence, explore it, and create your own applications
• Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time

Who This Book Is…

This resource is part of a series on specific topics related to data science: regression, clustering, neural networks, deep learning, Hadoop, decision trees, ensembles, correlation, outliers, regression, Python, R, Tensorflow, SVM, data reduction, feature selection, experimental design, time series, cross-validation, model fitting, dataviz, AI and many more. To keep receiving these articles, …