Neural networks are a rapidly developing paradigm for information processing based loosely on how neurons in the brain processes information. A neural network consists of multiple layers of node, where each node performs a unit of computation, and passes the result onto the next node. Multiple nodes can pass inputs to a single node, and vice-versa. The neural network also contains a set of weights, which can be refined over time as the network learns from sample data. The weights are used to describe and refine the connection strengths between nodes.

Artificial Intelligence has been threatening to transform the Contact Centre. But, how much of this talk is hype? And, how much is likely to happen? Could this lead to the decline of contact centres as we know it? Join us for this Webinar as we debunk the myths from the reality.

Machine learning is a type of artificial intelligence that uses algorithms to iteratively learn from data and finds hidden insights in data without being explicitly programmed where to look or how to find the answer. Here at SAS, we hear questions every day about machine learning: what it is, how it compares to other technologies, and why it matters. On the more technical end of the spectrum, we also hear questions about specific machine learning algorithms and how to use them. You will get all of your answers - and more - if you take the time to read the articles listed here, from definitions and comparisons to how-to tips on data science techniques. This isn't a comprehensive list, of course, but I tried to pick some favorite articles I've seen published over the last year, and I'm trying to list them here in order from introductory to more advanced.

Python is an easy to learn, powerful programming language. It has efficient high-level data structures and a simple but effective approach to object-oriented programming. Python's elegant syntax and dynamic typing, together with its interpreted nature, make it an ideal language for scripting and rapid application development in many areas on most platforms. The Python interpreter and the extensive standard library are freely available in source or binary form for all major platforms from the Python Web site, https://www.python.org/, The same site also contains distributions of and pointers to many free third party Python modules, programs and tools, and additional documentation.

I'm a developer from the UK who loves technology and business. Here you'll find articles and tutorials about things that interest me.

With the Paris Agreement still maintaining a dominant voice in social media and news, and the SDGs placing a strong emphasis on business to lead the way, eRevalue is offering a user-friendly and efficient solution to a complicated problem. The award winning AI technology company is launching a Sustainable Development Goals (SDGs) radar in April this year, to compliment its business intelligence tool Datamaran . The'SDG radar' was pre-launched with several exclusive eRevalue clients in late 2016. In April 2017, this unique capability will be available for companies everywhere. On March 7th, 2017, high level speakers from the UN Environment, the Global Compact Network Canada, Scotiabank and eRevalue will be running a webinar exploring the role of business in achieving the SDGs. The expert panel will discuss their perspective on what businesses can do to implement the SDGs – and the role of technology in assisting this process.

We develop and analyze efficient "coordinate-wise" methods for finding the leading eigenvector, where each step involves only a vector-vector product. We establish global convergence with overall runtime guarantees that are at least as good as Lanczos's method and dominate it for slowly decaying spectrum. Our methods are based on combining a shift-and-invert approach with coordinate-wise algorithms for linear regression.

Normally it would be considered rude to call a class "bullshit," but here's one time you can get away with it. Two University of Washington professors are teaching a course to help students "think critically about the data and models that constitute evidence in the social and natural sciences," according to the introduction to the course. The 160-seat seminar, titled "Calling Bullshit in the Age of Big Data," begins in late March and continues for roughly 10 weeks. Members of the general public can follow the course syllabus, including readings and recordings of lectures, at the course's website. At the end of the course, students should be able to "provide your crystals-and-homeopathy aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit," according to the syllabus.

After completing Part 1 of Jeremy Howard's awesome deep learning course, I took a look at my AWS bill and found I was spending nearly $200/month running GPUs. It's not necessary to spend that much to complete his course, but I started working on a few extracurricular datasets in parallel and I was eager to get results. After talking with fellow students and reading a number of blog posts, I decided to try building my own box. Technology and hardware change so rapidly that I'm afraid much of post will become outdated soon, but I hope my general approach will still be useful for at least a little while. I started by reading a bunch of blogs to get the current consensus on which parts to buy.

Say you have a collection of customers with a variety of characteristics such as age, location, and financial history, and you wish to discover patterns and sort them into clusters. Or perhaps you have a set of texts, such as wikipedia pages, and you wish to segment them into categories based on their content. This is the world of unsupervised learning, called as such because you are not guiding, or supervising, the pattern discovery by some prediction task, but instead uncovering hidden structure from unlabeled data. Unsupervised learning encompasses a variety of techniques in machine learning, from clustering to dimension reduction to matrix factorization. In this course, you'll learn the fundamentals of unsupervised learning and implement the essential algorithms using scikit-learn and scipy. You will learn how to cluster, transform, visualize, and extract insights from unlabeled datasets, and end the course by building a recommender system to recommend popular musical artists.