Deep data science is a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation, or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus these techniques also belong to deep data science. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation. No overlap with other fields such as statistics or machine learning. Other words for DDS (deep data science) could be pure data science or core data science.

Granville V., Rasson J.P. Multivariate discriminate analysis and maximum penalized likelihood.... Journal of the Royal Statistical Society, Series B, 57 (1995), 501-517.

Artificial intelligence (AI), deep learning, and neural networks represent incredibly exciting and powerful machine learning-based techniques used to solve many real-world problems. For a primer on machine learning, you may want to read this five-part series that I wrote. While human-like deductive reasoning, inference, and decision-making by a computer is still a long time away, there have been remarkable gains in the application of AI techniques and associated algorithms. The concepts discussed here are extremely technical, complex, and based on mathematics, statistics, probability theory, physics, signal processing, machine learning, computer science, psychology, linguistics, and neuroscience. That said, this article is not meant to provide such a technical treatment, but rather to explain these concepts at a level that can be understood by most non-practitioners, and can also serve as a reference or review for technical folks as well.

OpenAI, the nonprofit backed by Elon Musk and Peter Thiel to promote artificial intelligence that helps rather than harms humanity, opened a new virtual training center on Monday. It's called Universe, and anyone building artificial intelligence programs can use it. With Universe, developers can train artificial intelligence applications with games, websites, web browsers and other apps. The idea here is that the more an AI system practices using interfaces designed for human users, the more human-like AI can become. But since Universe is open for anyone to use, that leaves the door open to developers who may utilize Universe to train AI in a way that would beget harm -- precisely what Musk's nonprofit aims to prevent.

Amazon wants customers to be able to leave their wallets at home and still go shopping, as long as they have their smartphones on hand. The online retail giant is opening up a brick-and-mortar store in Seattle called Amazon Go, and the store uses a mix of sensors and computer learning to track what items customers pick up and to automatically add them to their Amazon accounts. Amazon identifies each customer using a QR code, which individuals scan before entering the store. The store offers groceries, ready-made food, and Amazon meal kits. Currently, Amazon Go is only accessible to the company's employees, but Amazon says it will open its doors to the public in early 2017.

I am a Machine Learning Engineer, Deep Learning Engineer and even an Indie Game Developer with a Major in Compilers and a Master's degree in Artificial Intelligence from University Politehnica of Bucharest. I am passionate about Games and Artificial Intelligence. I love to give life to A.I. agents in my project or my friend's projects and I want to teach you too.

In this article, we revisit the most fundamental statistics theorem, talking in layman terms. We investigate a special but interesting and useful case, that is not discussed in textbooks, data camps, or data science classes. This article is part of a series about off-the-beaten-path data science and mathematics, offering a fresh, original and simple perspective on a number of topics. Previous articles in this series can be found here and also here. The theorem discussed here is the central limit theorem. It states that if you average a large number of well behaved observations or errors, eventually, once normalized appropriately, it has a standard normal distribution.

This paper studies the performance of a recently proposed preconditioned stochastic gradient descent (PSGD) algorithm on recurrent neural network (RNN) training. PSGD adaptively estimates a preconditioner to accelerate gradient descent, and is designed to be simple, general and easy to use, as stochastic gradient descent (SGD). RNNs, especially the ones requiring extremely long term memories, are difficult to train. We have tested PSGD on a set of synthetic pathological RNN learning problems and the real world MNIST handwritten digit recognition task. Experimental results suggest that PSGD is able to achieve highly competitive performance without using any trick like preprocessing, pretraining or parameter tweaking.

Data Science Middle East Foundation in partnership with EVERATI running 3-day training workshop series across Middle East to get you started on your data science and machine learning journey, as you learn how to use data and science to deliver insights, value and innovation. Data Science and Machine Learning workshop is a 3-day practical training program for applied introduction to data science industry practices and models of machine learning. The workshop has a strong focus on gaining hands-on experience implementing algorithms and building predictive models on real datasets. By the end of the workshop, participants will be ready to implement the machine learning algorithms using data science on their own data, and immediately generate business value. The workshop will take participants through the conceptual and applied foundations of the subject.

Amazon wants customers to be able to leave their wallets at home and still go shopping, as long as they have their smartphones on hand. The online retail giant is opening up a brick-and-mortar store in Seattle called Amazon Go, and the store uses a mix of sensors and computer learning to track what items customers pick up and to automatically add them to their Amazon accounts. Amazon identifies each customer using a QR code, which individuals scan before entering the store. The store offers groceries, ready-made food, and Amazon meal kits. Currently, Amazon Go is only accessible to the company's employees, but Amazon says it will open its doors to the public in early 2017.