Many more, posted in the last 7 days, can be found here. They cover topics such as deep learning, data science, machine learning, Python, Tensorflow, AI, IoT, R, time series, Hadoop, data lakes, and more.

Deep learning is a field with intense computational requirements and the choice of your GPU will fundamentally determine your deep learning experience. With no GPU this might look like months of waiting for an experiment to finish, or running an experiment for a day or more only to see that the chosen parameters were off. With a good, solid GPU, one can quickly iterate over deep learning networks, and run experiments in days instead of months, hours instead of days, minutes instead of hours. So making the right choice when it comes to buying a GPU is critical. So how do you select the GPU which is right for you? This blog post will delve into that question and will lend you advice which will help you to make choice that is right for you. TL;DR Having a fast GPU is a very important aspect when one begins to learn deep learning as this allows for rapid gain in practical experience which is key to building the expertise with which you will be able to apply deep learning to new problems.

The AI will have to be accountable, so different from DL, with outcomes you can explain to average judges and users in simple, legally valid words. Even where humans take final decisions, the AI tools should give detailed reasons that humans can either figure out as wrong (and so override, reverse the AI decision), or quickly accept by simply copy, paste and sign explanations prepared by AI. In the case of GDPR, only human staff can reject an application: the AI can automate the positive outcomes, else, if the AI denies a loan, job etc., it should pass the task to human staff, that will handle those negative decisions that make users angry, inquisitive. The risk is that the human staff, to save time and money, will make up fake explanations for AI rejections, and blindly accept AI approvals.

For more on using deep learning to develop digital experiences, check out Danny Lange's session "Bringing gaming, VR, and AR to life with deep learning" at the Artificial Intelligence Conference, September 17-20, 2017, in San Francisco. Game development is a complex and labor-intensive effort. Game environments, storylines, and character behaviors are carefully crafted, requiring graphics artists, storytellers, and software engineers to work in unison. Often, games end up with a delicate mix of hard-wired behavior in the form of traditional code and a somewhat more responsive behavior in the form of large collections of rules. Over the last few years, data intensive machine learning (ML) solutions have obliterated rule-based systems in the enterprise--think Amazon, Netflix, and Uber. At Unity, we have explored the use of some of these technologies, including deep learning in content creation and deep reinforcement learning in game development.

Artificial Intelligence is getting hip by learning about tattoos. A couple of developers for the app Tattoodo wanted a better way to categorize all the tat pics they receive, so they built an algorithm. The pair created a neural-network and taught it how to use an iPhone camera to determine the style of a tattoo. The process involved using a deep-learning framework called Caffe, and feeding it data-sets with images representative of different tattoo styles. Once the initial training session was complete, the AI could identify the style of a tattoo with pretty impressive accuracy.

Artificial Intelligence, Machine Learning and Deep Learning are all the rage in the press these days, and if you want to be a good Data Scientist you're going to need more than just a passing understanding of what they are and what you can do with them. There are loads of different methodologies, but for me I would always suggest Artificial Neural Networks as the first AI to learn - but then I've always had a soft spot for ANNs since I did my PhD on them. They've been around since the 1970s, and until recently have only really been used as research tools in medicine and engineering. Google, Facebook and a few others, though, have realised that there are commercial uses for ANNs, and so everyone is interested in them again. When it comes to algorithms used in AI, Machine Learning and Deep Learning, there are 3 types of learning process (aka'training').

First it was Machine Learning, and now all of a sudden Deep Learning is taking all the thunder even from Machine Learning. So what's the difference, and why all of a sudden Deep Learning has become the most buzzing new Technology of our Era? Is Deep Learning a false idol being Ubiquitously worshiped or is it the panacea which is going to take us to our Utopia? These are two extreme positions & the reality usually lies somewhere in between. To delve deeply on this question, we need to first know what Machine Learning & Deep Learning are, & the difference between the two.

Cybercrime is estimated to cost the global economy 400 billion dollars (source McAfee). While fraud detection techniques have been used for decades, the industry now faces new challenges. Artificial Intelligence (AI) techniques are proposed to overcome the increasing challenges of online fraud. AI techniques are gaining popularity due to the power of Deep Learning Algorithms. But what does it mean to use Artificial Intelligence for fraud detection in practice?

IMAGE: Scientists will use ORNL's computing resources such as the Titan supercomputer to develop deep learning solutions for data analysis. A team of researchers from Oak Ridge National Lab oratory has been awarded nearly $2 million over three years from the Department of Energy to explore the potential of machine learning in revolutionizing scientific data analysis. The Advances in Machine Learning to Improve Scientific Discovery at Exascale and Beyond (ASCEND) project aims to use deep learning to assist researchers in making sense of massive datasets produced at the world's most sophisticated scientific facilities. Deep learning is an area of machine learning that uses artificial neural networks to enable self-learning devices and platforms. The team, led by ORNL's Thomas Potok, includes Robert Patton, Chris Symons, Steven Young and Catherine Schuman.

Deep convolutional networks have witnessed unprecedented success in various machine learning applications. Formal understanding on what makes these networks so successful is gradually unfolding, but for the most part there are still significant mysteries to unravel. The inductive bias, which reflects prior knowledge embedded in the network architecture, is one of them. In this work, we establish a fundamental connection between the fields of quantum physics and deep learning. We use this connection for asserting novel theoretical observations regarding the role that the number of channels in each layer of the convolutional network fulfills in the overall inductive bias.