Mercedes-Benz, whose engineers have been working on self-driving car technology, is eager to increase the size of its engineering team both in Silicon Valley and in Germany. SAN FRANCISCO - So you say you want join the automotive revolution? Over the past few years, only elite roboticists have been positioned to heed the self-driving car's call to action. Armed with degrees from places such as Carnegie Mellon University and experience at institutions such as NASA, these tech titans have been highly sought after by technology and automotive companies looking to build the future. But now massive open online course pioneer Udacity has a proposition: Give the Web-based education outfit 36 weeks and 2,400, and they'll turn graduates onto jobs at autonomous-car partner companies Mercedes-Benz, Didi Chuxing, Nvidia and Otto.

The corporate learning environment has been experiencing a great deal of development over recent years. It shows no signs of stopping as learners become more involved in their own education. Gone are the days when the organization dictated what should be learned and how. Learners are now demanding that they are educated in a way that works for them. The teams usually responsible for corporate learning within companies, human resources, are also undergoing a period of change as they identify areas where they need to come up to speed to deliver the most tangible results for their companies.

Deep Learning: Keras Short Video Tutorial (youtu.be)

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

As Torrance observes: '[c]reativity defies precise definition... even if we had a precise conception of creativity, I am certain we would have difficulty putting it into words' [15, p. 43]. Many other authors have expressed similar difficulties [7, 10, 16]. In their review of research into human creativity, Hennessey and Amabile ask a significant follow-on question: 'Even if this mysterious phenomenon can be isolated, quantified, and dissected, why bother? Wouldn't it make more sense to revel in the mystery and wonder of it all?' [11, p. 570] Two answers to this question are offered by Hennessey and Amabile, both of which are identified as desirable: to gain a deeper understanding of creativity and to learn how to boost people's creativity. Creativity can and should be studied and measured scientifically, but the lack of a commonly-agreed understanding causes problems for measurement [10]. Plucker et al. make recommendations about best practice based on their own survey of the creativity literature: 'we argue that creativity researchers must (a) explicitly define what they mean by creativity, (b) avoid using scores of creativity measures as the sole definition of creativity (e.g., creativity is what creativity tests measure and creativity tests measure creativity, therefore we will use a score on a creativity test as our outcome variable), (c) discuss how the definition they are using is similar to or different from other definitions, and (d) address the question of creativity for whom and in what context.' [9, p.92] In short, we need to specify and justify the standards that we use to judge creativity. A more objective and well-articulated account of how creativity is manifested enables researchers to make a worthwhile contribution [8-10]. Particularly, in research we would like to focus on what processes and concepts relevant to creativity are'sufficiently important to warrant study' [17, p. 15], based on an accumulation of the body of work on creativity to date [17].

During the christmas vacation of 2015, I got a motivational boost again and decided try out Kaggle. So I spent quite some time experimenting with various algorithms for their Homesite Quote Conversion, Otto Group Product Classification and Bike Sharing Demand contests. The main takeaway from this was the experience of iteratively improving the results by experimenting with the algorithms and the data. I learned to trust my logic when doing machine learning. If tweaking a parameter or engineering a new feature seems like a good idea logically, it's quite likely that it actually will help.

"The second edition was reviewed in JASA by Maiti (2004) … we now stand 10 years later with an even more impressive textbook that truly stands for what Bayesian data analysis should be. Quite a lot … this is truly the reference book for a graduate course on Bayesian statistics and not only Bayesian data analysis." Praise for the Second Edition: … it is simply the best all-around modern book focused on data analysis currently available. The second edition makes this an even more robust choice. I am thoroughly excited to have this book in hand to supplement course material and to offer research collaborators and clients at our consulting lab more sophisticated methods to solve their research problems.

Google has reached a milestone in its DeepMind artificial intelligence (A.I.) project with the successful development of technology that can mimic the sound of human voice. Dubbed as WaveNet, the breakthrough was described as a deep neural network that can generate raw audio wave forms to generate speech. It can reportedly beat existing Text-to-Speech systems. According to researchers in the Britain-based WaveNet unit, the gap in human performance, which could be demonstrated in an actual A.I. -- human conversation -- is reduced by as much as 50 percent. What is also interesting about the WaveNet technology is that it is capable of learning different voices and speech patterns to the point that it can even simulate mouth movements and artificial breaths in addition to emotions, language inflections and accents.

This is the last of three articles about performance measures and graphs for binary learning models in Azure ML. Binary learning models are models which just predict one of two outcomes: positive or negative. These models are very well suited to drive decisions, such as whether to administer a patient a certain drug or to include a lead in a targeted marketing campaign. This final article will cover the threshold setting, and how to find the optimal value for it. As you will learn, this requires a good understanding of error cost, that is, the cost of inaccurate predictions.

Artificial intelligence (or AI) has permeated most facets of our lives. Algorithms suggest our social media mates. But could the arrival of the robots be applied to education? Jozef Misik, managing director of Knowble, a language tech start-up whose products are built on AI, believes so: "Most educational technology products will have an AI or deep learning component in future," he says. Already, AI is able to address common learning challenges.