Summary:  GANs (Generative Adversarial Nets) originally thought to be a tool for inexpensively creating DNN training data have instead become the tools for creating deep fake images.  However the deep fake technology is now finding uses in the commercial world and promises valuable results.

Generative…

We propose a simple model-free solution to compute any confidence interval and to extrapolate these intervals beyond the observations available in your data set. In addition we propose a mechanism  to sharpen the confidence intervals, to reduce their width by an order of magnitude. The methodology works with any estimator (mean, median, variance, quantile, correlation and so on) even when the data set violates the classical requirements necessary to make traditional statistical techniques…

Naive Bayes is a deceptively simple way to find answers to probability questions that involve many inputs. For example, if you're a website owner, you might be interested to know the probability that a visitor will make a purchase. That question has a lot of "what-ifs", including time on page, pages visited, and prior visits. Naive Bayes essentially allows you to take the raw inputs (i.e. historical data), sort the data into more meaningful chunks, and input them into a formula. …

Summary:  There are several approaches to reducing the cost of training data for AI, one of which is to get it for free.  Here are some excellent sources.

Recently we wrote that training data (not just data in general) is the new oil.  It’s the difficulty…

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A complete guide to K-means clustering algorithm

The gradient descent algorithm is one of the most popular optimization techniques in machine learning. It comes in three flavors: batch or “vanilla” gradient…

Artificial Intelligence (AI) and the technologies associated with it have disrupted the market and created room for bigger and better opportunities. Based on the insights from the data, these technologies require a lot of trust and assurances. Since organizations are…

The concept of efficiency is not always easy to grasp.  People go to work.  They do their job.  They follow the pace.  What exactly is efficiency in the scheme of things?  I think it is important to be able to distinguish between how hard different workers are working.  It is not really possible to discuss efficiency if this kind of comparison is not performed.  Improving efficiency is about getting more from a person or process.  Therefore in order to determine whether or not efficiencies…

"AI will be the most defining technology for the banking industry."

-Ravi Narayanan, HDFC Bank

Such a comment, coming from an organization (HDFC) which has taken a gigantic leap in adopting conversational banking in the form Eva, India’s first AI bank agent, isn’t surprising. However, make no mistake, AI is being accepted globally as the new UI for banks to interact with their customers. Industry thought leaders increasingly agree that…

Machine learning gives us the ability to train a model, which can convert data rows into labels in such a way that similar data rows are mapped to similar or the same label.

For example, we are building SPAM filter for email…

Artificial intelligence (AI) seemingly has been discussed everywhere over the last few years, and now it’s made its way into the commercial insurance industry. Organizations are using AI and machine learning for everything from streamlining operations to offering more personalized care and better customer service. There is an increasing sense of urgency about getting started on the AI journey. The question is how. Do they develop a custom solution in-house or purchase a third-party solution…

This crash course features a new fundamental statistics theorem -- even more important than the central limit theorem -- and a new set of statistical rules and recipes. We discuss concepts related to determining the optimum sample size, the optimum k in k-fold cross-validation, bootstrapping, new re-sampling techniques, simulations, tests of hypotheses, confidence intervals, and statistical inference using a unified, robust, simple approach with easy formulas, efficient…

Introduction

It is a well-known fact that neural networks can approximate the output of any continuous mathematical function, no matter how complicated it might be. Take for instance the function below:…

Everything you need to know about a top algorithm in Machine Learning

Did you ever have a concept that you knew was right, but just couldn’t find the right words to articulate that concept?  Okay, well welcome to my nightmare.  I know that Data Science and Design Thinking share many common characteristics including the power of “might” (i.e., that “might” be a better predictor of performance), “learning through failing” (which is the only way to determine where the edges of the solution really reside), and the innovation liberation…

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