Summary:  The annual Burtch Works salary survey with data through April shows that opportunities and salaries are still excellent for both new and experienced data scientists.  They also offer some anecdotal observations about the impact of the first few months of COVID on our work and opportunities.

 …

AI for People and Business is an AI book and AI strategy framework. Here's the official book trailer where I explain important topics that are covered in the book, as well as the beneficial aspects of reading it and the importance of developing a strong AI strategy.

FREE CHAPTER DOWNLOAD -…

Summary:  Transfer Learning (TL) may be the most important aid to adoption of deep learning in the last several years.  This new LEEP measure predicts the accuracy of the transfer and should make TL faster, cheaper, and better.

What is the single most important innovation in deep learning in the last several…

Machine learning is tied in with creating predictive models from uncertain data. Uncertainty implies working with imperfect or fragmented information.

In any case, we can oversee uncertainty utilizing the tools of probability.…

In the past few years, Machine Learning has rapidly changed our lives with its efficient applications. Thi technology has given an uplift to the entire mobile industry. Making devices and mobile apps as intelligent as humans, machine learning is…

Summary:  Less than 9%?  What this study really shows and what we should take away from it.

Wow.  Less than 9%!  Can this be true?  Well according to a large scale survey study conducted by the US Census Bureau it’s actually a…

If you were to invest in real estate, what would be the most important factor you’d take into account? Would it be the age of the building, its location, or maybe how many owners it previously had?

While all of the above will certainly be important to you (though to varying extents), there’s one universal factor that will either be a deal-breaker or make you want to go all in.

You’ve guessed it — the price.

We all have a rough overview of…

Dropout means to drop out units which are covered up and noticeable in a neural network. Dropout is a staggeringly in vogue method to overcome overfitting in neural networks.

Deep Learning framework is now getting further and more profound. With these bigger networks, we can accomplish better prediction exactness. However, this was not the case a few years ago. Deep…

Summary:  Bias in modeling has long been a public concern that is now amplified and focused on the disparate treatment models may cause for African Americans.  Defining and correcting the bias presents difficult issues for data scientists that need to be carefully thought through before reaching conclusions.

 …

By

• Priya Sharma – Sr. Data Scientist -IoT Analytics, SAS Institute Inc.
• Saurabh Mishra – Product Management, IoT, SAS Institute Inc.

June 12, 2020

Description: Majority of AI approaches are based on the construct of training against historical data and then inferencing new data. While this is a sound and proven approach, a lot of IoT assets coming online don’t have historical data and we don’t necessarily have the time to wait.

Modern Machine…

This the second part of Reinforcement Learning (Q-learning). If you would like to understand the RL, Q-learning, and key terms please read Part 1.

In this part, we will implement a simple example of Q learning using the R programming language from scratch. It is expected from you to understand the basics of R programming and complete the reading of Part 1 of this article.

Import libraries

We are coding the algorithms using the R base package…

Have you heard about AI learning to play computer games on their own and giving tough competitions to expert Human gamers?

A very popular example being Deepmind whose AlphaGo program defeated the South Korean Go world champion in 2016. Other than this there are other AI agents developed with the intent of playing Atari games like…

Summary:  Explaining data science to a non-data scientist isn’t as easy as it sounds.  You may know a lot about math, tools, techniques, data, and computer architecture but the question is how do you explain this briefly without getting buried in the detail.  You might try this approach.

We’ve all been…

Summary:  What is an AI Product Manager and how do you know when you need one.

The role of Product Manager (PM) can mean many things dependent on the specifics of the company, its markets, its channels, and the variety of its products.  It’s almost impossible to put a single label on the responsibilities of a…

In the article, we talk about the benefits of gamification in training, why it’s becoming a necessity in the Education field and what are some brightest examples of gamification software across the world.

Why is education not effective without gamification? Psychology, social impact and technology hidden…

Summary:  In a comprehensive study of 18 recently presented DNN advancements in top-N recommenders, only 7 presented sufficient data to allow reproduction.  Worse, of the 7 that could be reproduced none showed an actual improvement over simple linear and KNN techniques when those were properly optimized.

 …

How to maintain Model Effectiveness after deployed

When we ready to deploy a good predictive model which given a good accuracy score on training and testing dataset, there is one more problem to solve.  How long will this model effectively solve the problem with the same high accuracy and what is the strategy to maintain the model accuracy. We also need to…

Summary: As we have become ever more enamored with DNNs, and their accuracy and utility has been paced only by their complexity we will need to answer the question of whether we will ever really be able to explain what goes on inside.

As we have become ever more enamored with DNNs, and their accuracy and…

Summary:  Analytic Platforms are rapidly being augmented with features previously reserved for data scientists.  They are presented as easy to use but require substantial data literacy and advanced DS skills for the most complex.  Business users and analysts can pursue more complex problems on their own, but need good oversight.

 …