Due to the recent achievements of artificial neural networks across many different tasks (such as face recognition, object detection and Go), deep learning has become extremely popular. This post aims to be a starting point for those interested in learning more about it. If you already have a basic understanding of linear algebra, calculus, probability and programming: I recommend starting with Stanford's CS231n. The course notes are comprehensive and written well. The slides for each lesson are also available, and even though the accompanying videos were removed from the official site, re-uploads are quite easy to find online.

The X Prize Foundation and IBM have officially launched a global artificial intelligence competition that provides participants with the chance to compete for 5 million. The contest, dubbed "IBM Watson A.I. XPRIZE: A Cognitive Computing Competition," was announced this morning at TED2016. The competition invites teams from around the world to harness the power of artificial intelligence (AI) in order to brainstorm a solution to some of the planet's most pressing problems. "X Prize believes that artificial intelligence is the best tool in our toolkit to address the world's grand challenges and biggest problems," said Stephanie Wander, X Prize development associate and lead prize designer, to IFLScience. Which grand problem will the developers have to tackle?

This repository contains Python code for a selection of tables, figures and LAB sections from the book'An Introduction to Statistical Learning with Applications in R' by James, Witten, Hastie, Tibshirani (2013). This great book gives a thorough introduction to the field of Statistical/Machine Learning. The book is available for download (see link below), but I think this is one of those books that is definitely worth buying. The book contains sections with applications in R based on public datasets available for download or which are part of the R-package ISLR. Furthermore, there is a Stanford University online course based on this book and taught by the authors (See course catalogue for current schedule).

To recap the last tutorial: we got comfortable with Python for re-implementing the models we originally imagined in Excel. By using a programming language, we were able to (1) use more powerful constructs and methods, like arrays to store and retrieve variables, and (2) to write scripted steps that can be repeated in the future without us performing the work by hand. However, you may be thinking that you found it easier to understand what's in the data back when you were using Excel. Well, in this third tutorial we will take a slight detour from our modeling work in order to bridge that gap. Python has another great package called Pandas, which makes data exploration and data cleaning much easier to do than manipulating arrays.

Finding water on Mars has been a dream of scientists for decades and could prove an essential resource for any human pioneers looking to colonise the red planet. Now Nasa is hoping its Curiosity Rover may finally be able to settle the question of whether the liquid still exists on the planet's surface. Scientists are dispatching the robot to explore a feature on a sandy hillside they believe could be their best chance yet of finding water on the red planet. Scientists are hoping to send the Curiosity Rover to investigate dark streaks that appear on the nearby Mount Sharp during the Martian summer. Last year Nasa confirmed'dark fingers' spotted by the Mars Reconnaissance Orbiter were likely to be made by liquid moving across the planet's surface.

In this week's Brain Boost: Adidas' robots, AI and humans, and getting started with crowdfunding. Adidas' Robots Robotic manufacturing is going beyond cars for the first time. Adidas, the German sportswear company, announced that the first ever robotic reproduced shoe line will be available to customers in 2017 and, though Adidas is not aiming for full robotic automation, it is looking to learn more about the possibilities of robotic manufacturing, starting with this for a series of shoes. AI Humans However, on the other side of the Atlantic, the 2016 Exponential Manufacturing Conference wrapped up and one of the world's foremost data scientists, Professor Sandy Pentland of MIT, says that artificial intelligence is best used in collaboration with humans. Professor Pentland says that one of the most common challenges to large organizations is delivering the right information to the right people at the right time and he's proposing that software developers use artificial intelligence programs to predict who needs what data when and where and then use the internal computing systems to deliver key figures, best practices, etc. at the right time to the right people.

Studies have illustrated the power of priming to affect the way we feel about certain stimuli. In 2008, researchers at the University of Chicago conducted a study that used the American flag to discover how small environmental factors influence political judgment. Two versions of an online survey were distributed to both Democratic and Republican voters, one including a small American flag and one without. The study found that voters who were exposed to the presence of the flag tended to favor the Republican Party, even up to eight months after taking the original survey.

CVPR 2016 -- CEVA, Inc. (NASDAQ: CEVA), the leading licensor of signal processing IP for smarter, connected devices, today introduced CDNN2 (CEVA Deep Neural Network), its second generation neural network software framework for machine learning. CDNN2 enables localized, deep learning-based video analytics on camera devices in real time. This significantly reduces data bandwidth and storage compared to running such analytics in the cloud, while lowering latency and increasing privacy. Coupled with the CEVA-XM4 intelligent vision processor, CDNN2 offers significant time-to-market and power advantages for implementing machine learning in embedded systems for smartphones, advanced driver assistance systems (ADAS), surveillance equipment, drones, robots and other camera-enabled smart devices. CDNN2 builds on the successful foundations of CEVA's first generation neural network software framework (CDNN), which is already in design with multiple customers and partners.

Deep learning is the new big trend in machine learning. It had many recent successes in computer vision, automatic speech recognition and natural language processing. The goal of this blog post is to give you a hands-on introduction to deep learning. To do this, we will build a Cat/Dog image classifier using a deep learning algorithm called convolutional neural network (CNN) and a Kaggle dataset. This post is divided into 2 main parts. The first part covers some core concepts behind deep learning, while the second part is structured in a hands-on tutorial format. In the first part of the hands-on tutorial (section 4), we will build a Cat/Dog image classifier using a convolutional neural network from scratch.

Artificial Intelligence has touched each and every sector. We all know about Facebook suggesting friends or Siri managing our calendars. Even the stocks are traded by computers. There are cars which can park on their own. From education to military to even manufacturing there is hardly a field where artificial intelligence does not pay a role.