This week MapR announced a new solution called Quick Start Solution (QSS), focusing on deep learning applications. MapR touts QSS as a distributed deep learning (DL) product and services offering that enables the training of complex deep learning algorithms at scale. Here's the idea: deep learning requires lots of data, and it is complex. If MapR's Converged Data Platform is your data backbone, then QSS gives you what you need to use your data for DL applications. It makes sense, and it is in line with MapR's strategy.

For CLEVR dataset, they used LSTM to encode the question. For Sort-of-CLEVR dataset, which is much simpler than actual CLEVR dataset, the authors encoded question into vector for simplicity.

Deep Learning, whether you like it or not is here to stay, and with any tech gold-rush comes a plethora of options that can seem daunting to newcomers. If you were to start off with deep learning, one of the first questions to ask is, which framework to learn? I'd say instead of a simple trial-and-error, if you try to understand the building blocks of all these frameworks, it would help you make an informed decision. Common choices include Theano, TensorFlow, Torch, and Keras. All of these choices have their own pros and cons and have their own way of doing things.

The best artificial intelligence still has trouble visually recognizing many of Homer Simpson's favorite behaviors such as drinking beer, eating chips, eating doughnuts, yawning, and the occasional face-plant. Those findings from DeepMind, the pioneering London-based AI lab, also suggest the motive behind why DeepMind has created a huge new dataset of YouTube clips to help train AI on identifying human actions in videos that go well beyond "Mmm, doughnuts" or "Doh!" The most popular AI used by Google, Facebook, Amazon, and other companies beyond Silicon Valley is based on deep learning algorithms that can learn to identify patterns in huge amounts of data. Over time, such algorithms can become much better at a wide variety of tasks such as translating between English and Chinese for Google Translate or automatically recognizing the faces of friends in Facebook photos. But even the most finely tuned deep learning relies on having lots of quality data to learn from.

TL;DR: Computer scientists have adapted a widely used technique for rapid data-lookup to slash the amount of computation -- and thus energy and time -- required for'deep learning.' This applies to any deep-learning architecture, and the technique scales sublinearly, which means that the larger the deep neural network to which this is applied, the more the savings in computations there will be," said lead researcher Anshumali Shrivastava, an assistant professor of computer science at Rice. The research will be presented in August at the KDD 2017 conference in Halifax, Nova Scotia.

With growing interest in neural networks and deep learning, individuals and companies are claiming ever-increasing adoption rates of artificial intelligence into their daily workflows and product offerings. Coupled with breakneck speeds in AI-research, the new wave of popularity shows a lot of promise for solving some of the harder problems out there. That said, I feel that this field suffers from a gulf between appreciating these developments and subsequently deploying them to solve "real-world" tasks. A number of frameworks, tutorials and guides have popped up to democratize machine learning, but the steps that they prescribe often don't align with the fuzzier problems that need to be solved. This post is a collection of questions (with some (maybe even incorrect) answers) that are worth thinking about when applying machine learning in production.

Artificial Intelligence, Machine Learning, and Deep Learning are revolutionizing the financial technology industry. Machine Learning and Deep Learning are a growing and diverse fields of Artificial Intelligence (AI) which studies algorithms that are capable of automatically learning from data and making predictions based on data. Machine Learning and Deep Learning are two of the most exciting technological areas of AI today. Each week there are new advancements, new technologies, new applications, and new opportunities. That's why we created this guide to help you keep pace with all these exciting developments.

Humankind lost another important battle with artificial intelligence (AI) last month when AlphaGo beat the world's leading Go player Ke Jie by three games to zero. AlphaGo is an AI program developed by DeepMind, part of Google's parent company Alphabet. Last year it beat another leading player, Lee Se-dol, by four games to one, but since then AlphaGo has substantially improved. AlphaGo will now retire from playing Go, leaving behind a legacy of games played against itself. They've been described by one Go expert as like "games from far in the future", which humans will study for years to improve their own play.

Two of the most important aspects of machine learning models are feature extraction and feature engineering. Those features are what supply relevant information to the machine learning models. If the features are few or irrelevant, your model may have a hard time making any useful predictions. If there are too many features, your model will be slow and likely overfit. Humans don't necessarily know what feature representation are best for a given task.

What are the natural extensions of machine learning (ML) and deep learning as well as natural language processing (NLP) and affective computing (AC)? To many people, what distinguish machines from humans is emotion. True, some existentialists might push the envelope and go so far as to say consciousness (which is a valid argument), but the primary existential reality is emotion. A computer is not a living entity, does not understand empathy and cannot gauge how we feel. It does not and cannot care whether its users are happy, sad, frustrated or simply regretting a heavy lunch.