Amazon's voice-controlled assistant should soon sound more natural to Brits. The company has upgraded the UK version of Alexa with "Speechcons," an extensive list of words and phrases that can be delivered in a more lively, expressive manner. These include "whoops a daisy," "bob's your uncle" and "oh my giddy aunt," as well as "crikey," "blimey" and "geronimo!" Speechcons are part of the Alexa development platform, meaning anyone can draw on them for third-party "skills." As long as it's wrapped in an "interjection" tag, Alexa will check the word against its Speechcon bank and, if it's listed, enunciate with a little extra oomph.

When I first started out learning about machine learning algorithms, it turned out to be quite a task to gain an intuition of what the algorithms are doing. Not just because it was difficult to understand all the mathematical theory and notations, but it was also plain boring. When I turned to online tutorials for answers, I could again only see equations or high level explanations without going through the detail in a majority of the cases. It was then that one of my data science colleagues introduced me to the concept of working out an algorithm in an excel sheet. And that worked wonders for me.

I provide some additional information on using RStudio in Appendix A. In this section we will discuss how you get data into and out of the Execute R Script module. We will review how to handle various data types read into and out of the Execute R Script module. The complete code for this section is in the zip file you downloaded earlier. We will start by loading the csdairydata.csv

Big data in machine learning is the future. But how to deal with data analysis and limited resources: Computational power, data distribution, energy or memory? From September 25th to 28th, 2017 TU Dortmund University, Germany, hosts the 4th summer school on resource-aware machine learning. Topics of the lectures include: Exercises help bringing the contents of the lectures to life. The PhyNode low power computation platform was developed at the collaborative research center SFB 876.

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 well-written. 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.

Deep learning is not a beginner-friendly subject -- even for experienced software engineers and data scientists. If you've been Googling this subject, you may have been confused by the resources you've come across. To find the best resources, we surveyed engineers on their favorite sources for deep learning, and these are what they recommended. These educational resources include online courses, in-person courses, books, and videos. All are completely free and designed by leading professors, researchers, and industry professionals like Geoffrey Hinton, Yoshua Bengio, and Sebastian Thrun.

A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures.

Previously, I had started this conversation with the saying "I am not a Geek, but I need a job too…". Here is why: Technological anxiety (oh yes, it is a thing). I don't want to be a victim of the inevitable wave of "robots taking over our jobs" which is a simplistic explanation for the impact of advancements in technology in the workplace. The idea that half of today's jobs may vanish has changed my view of my children's future. Quincy Larson, Teacher at FreeCodeCamp (an open-source community that helps you learn to code, build pro bono projects for nonprofits, and get a job as a developer) has not stopped in his attempt to get more people coding.

How can AI help enterprises be top of mind for most modern organizations? In this webinar, we will discuss how forward-thinking enterprises are using Infosys Mana, a next generation AI platform for the enterprises to augment automation with AI in order to reinvent systems and processes.

This course will get you started in building your FIRST artificial neural network using deep learning techniques. Following my previous course on logistic regression, we take this basic building block, and build full-on non-linear neural networks right out of the gate using Python and Numpy. All the materials for this course are FREE. We extend the previous binary classification model to multiple classes using the softmax function, and we derive the very important training methodcalled "backpropagation" using first principles. I show you how to code backpropagation in Numpy, first "the slow way", and then "the fast way" using Numpy features.