It's not just for developers with a farm of servers and GPUs, though--hobbyists and modest users can be equally competitive because the Toolkit is flexible enough to run on a single laptop. Developers can also integrate into the Toolkit their own Python or C code.

Some videos are available only by enrolling in a Coursera or EdX class. It is free to do so, but sometimes the classes are no longer in session so you have to wait a couple of months, so you have no access. I'm going to be adding more videos from public sources and replacing the online course videos over time. I like using university lectures.

Within the silos of incumbent financial services, so-called fintech companies are good at picking off one thing only and doing it well. This approach is also taken within data science, where a lot of the properly intelligent work is about understanding the domain (problem) and how best to use the information/data for the problem you have. In doing so, a fintech approach – collaboration, open-sourcing code – is helping to gradually change the culture of finance, even in some hitherto heavily guarded domains. Dr Tristan Fletcher, research director, Thought Machine, said: "Without this specialisation and domain knowledge, it's very hard to rise above the noise. However, the algorithms themselves are often applicable in many areas or problems, and we are probably seeing decreasing specialisation here. "Fintech lends itself particularly to specialisation because there are many well-packaged problems that need to be solved and can be clearly delineated – KYC, AML, credit checking etc.

This article is a part of an evolving theme. Here, I explain the basics of Deep Learning and how Deep learning algorithms could apply to IoT and Smart city domains. Specifically, as I discuss below, I am interested in complementing Deep learning algorithms using IoT datasets. I elaborate these ideas in the Data Science for Internet of Things program which enables you to work towards being a Data Scientist for the Internet of Things (modelled on the course I teach at Oxford University and UPM – Madrid).

A team of researchers from Microsoft, Cray, and the Swiss National Supercomputing Centre (CSCS) have been working on a project to speed up the use of deep learning algorithms on supercomputers. They accelerated the training process. Instead of waiting weeks or months for results, data scientists can obtain results within hours or even minutes. With the introduction of supercomputing architectures and technologies to deep learning frameworks, customers now have the ability to solve a whole new class of problems, such as moving from image recognition to video recognition, and from simple speech recognition to natural language processing with context. The team have scaled the Microsoft Cognitive Toolkit -- an open-source suite that trains deep learning algorithms -- to more than 1,000 Nvidia Tesla P100 GPU accelerators on the Swiss centre's Cray XC50 supercomputer, which is nicknamed Piz Daint.

I think there are three things that are going to affect the world in incredibly significant ways over the next decade and they are 1) Climate change 2) CRISPR and 3) artificial intelligence. I wanted to work in one of those and be helpful. Because of my background, AI made the most sense. Along with conducting fundamental research, OpenAI can also help increase the level of knowledge that's available on how to use, regulate and evaluate this technology. NIPS is probably the single largest AI conference in the field and it's happening in Barcelona right now. There's a joke among researchers that NIPS is where people get together to discuss papers that came out four months ago. That's because the paper deadline was then, and the pace of modern AI research is so fast that much of the industry has subsequently moved onto new techniques and new papers.

'Tis the season for the public relations exercise known as "here's what we think (or hope) will happen in the tech sector next year," flooding my inbox with predictions for 2017. No one knows what will happen tomorrow, let alone over the next 12 months, but the exercise yields interesting insights into what's hot (and what's not) in technology today. Artificial intelligence (and machine/deep learning) is the hottest trend, eclipsing, but building on, the accumulated hype for the previous "new big thing," big data. The new catalyst for the data explosion is the Internet of Things, bringing with it new cybersecurity vulnerabilities. The rapid fluctuations in the relative temperature of these trends also create new dislocations and opportunities in the tech job market.

Artificial intelligence will be key to optimizing big data, mobile, social media and the Internet of Things. Machine learning is being implemented in everything from warehouse management systems to cybersecurity platforms, accelerating the speed of innovation in IT. And Google, Facebook, Apple, Amazon and Microsoft are in a race to monetize AI. Microsoft announced this week that its venture arm has founded a fund that focuses solely on AI, and that its first investment will be in a startup called Element AI. Yoshua Bengio, a long-time pioneer in deep learning, started the Montreal-based lab and will receive an undisclosed sum from Microsoft Ventures.

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On Quora the other day I saw a question from an aspiring data scientist that asked – since all the competitions on Kaggle these days are being won by Deep Learning algorithms, does it even make sense to bother studying traditional machine learning methods? Has Deep Learning made traditional machine learning irrelevant? I can understand on a couple of levels why he asked the question. First if you look at the recently completed Kaggle problems it's easy to draw the conclusion that deep learning is the only way to win. Second, if you follow the data science literature we are being bombarded by information about advancements in deep learning, especially as it's implemented in AI, with very little new coming out about all the other algorithms that make up our tool kit.