Machine Learning Research Engineer wanted to join a leading research firm in London. You will be Implementing Machine Learning algorithms in real-world environments and help create impactful, currently non-existent solutions, which will have the potential to transform industries covering computer vision, Deep Learning, Theano, Tensorflow, biometrics, facial recognition, voice recognition and/or novelty/outlier detection.

Join us on 20, 21 and 22 February for the first Microsoft Most Valuable Professional (MVP) led TechDays Online of 2017. On day one, MVPs and community leaders will delve into the World of Mobile Development, BOTs and Data Science followed by day two, where MVPs from around the Globe will share their knowledge of cross platform development on Microsoft Azure. Day three will end this online event with a look to the future with Blockchain, Quantum Computing and Deep Learning. As always, each session will be led by MVPs from the UK and across the World, along with Microsoft Technical Evangelists. It is a three day online event'not to be missed'.

Over the past year I've reviewed half a dozen open source machine learning and/or deep learning frameworks: Caffe, Microsoft Cognitive Toolkit (aka CNTK 2), MXNet, Scikit-learn, Spark MLlib, and TensorFlow. If I had cast my net even wider, I might well have covered a few other popular frameworks, including Theano (a 10-year-old Python deep learning and machine learning framework), Keras (a deep learning front end for Theano and TensorFlow), and DeepLearning4j (deep learning software for Java and Scala on Hadoop and Spark). If you're interested in working with machine learning and neural networks, you've never had a richer array of options. Essentially, a machine learning framework covers a variety of learning methods for classification, regression, clustering, anomaly detection, and data preparation, and it may or may not include neural network methods. A deep learning or deep neural network (DNN) framework covers a variety of neural network topologies with many hidden layers.

For more practical techniques for getting started with deep learning, check out the deep learning sessions at Strata Hadoop World San Jose, March 13-16, 2017. Yes, you can run TensorFlow on a $39 Raspberry Pi, and yes, you can run TensorFlow on a GPU powered EC2 node for about $1 per hour. And yes, those options probably make more practical sense than building your own computer. But if you're like me, you're dying to build your own fast deep learning machine. OK, a thousand bucks is way too much to spend on a DIY project, but once you have your machine set up, you can build hundreds of deep learning applications, from augmented robot brains to art projects (or at least, that's how I justify it to myself).

Home Depot uses it to show which bathtubs in its huge inventory will fit someone's oddly shaped bathroom. Apple uses it to present customers with relevant apps from the app store. Intuit uses it to display the right help page when a user is filling out a particular tax form. And organizations are turning to it in droves to differentiate and innovate their offerings. In a recent interview, Gartner Fellow and Vice President Tom Austin noted that about half of large enterprises are experimenting with "smart computing" projects.

Companies looking to develop a full-fledged artificial intelligence solution have to focus on many different subfields. One of those fields is called deep learning, which is similar to machine learning up to a certain extend. However, deep learning is based on a set of algorithms which attempt to model abstractions found in data. A very complex part of developing AI, but one that is well worth exploring. The entire concept of machine learning is far more complex than people give it credit for.

Artificial intelligence has been the focus of technology research for quite a while now. Recent years have seen considerable development in machine learning, narrow AI and deep learning. What does 2017 promise us in the realm of AI? How closer could we come to the glorious liberating visions of AI that have been the subject of fantasy and disbelief in previous decades? In this article, we talk about the five most significant developments that might be realized in 2017. With easy commercialization, greater investment in research and applicability across sectors, this could be the year when AI finally takes off as a commercially viable part of the technology industry.

The year 2016 proved to be an important milestone in the history of Artificial Intelligence (AI). Though the researchers have been working on creating intelligent machines for long, the year 2016 witnessed the concept leaving the realm of science-fiction to become more tangible and realistic. The year began with the Facebook founder Mark Zuckerberg announcing his plans to build an artificial assistant for his home. As the year proceeded, organisations across the globe started increasingly investing their resources towards the research and development of AI. As the word caught on pace, tech titans including Google, Intel and Apple raced to acquire private companies working to advance artificial intelligence.

Many of us might have heard the terms AI, machine learning and deep learning. Some of us also might have heard that they can have a big impact on cyber security. What are AI, machine learning and deep learning actually? And, how can they improve cyber security? In this article we would discuss about that. Artificial Intelligence or AI is the science and engineering of making a machine intelligent, so that it can perform tasks similar to those that require human intelligence.