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.

Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

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.

At just 5ft, they may not seem like the most imposing security guards, but they could soon be patrolling shopping centres around the world. The Knightscope K5 robots are the creation of a Silicon Valley startup firm and have been specially designed for fighting crime. And the company says it has just signed a deal which will see the droids roll out across 16 cities. Knightscope has just signed a deal which will see its K5 security droids (pictured) roll out to shopping malls across 16 cities in the United States. The K5 crime-fighting robots are 5ft tall and come with GPS, lasers, and heat-detecting technology.

Researchers have known that "artificial neurons" could carry out logical functions--i.e., learn the way humans do--since 1943. The term "artificial intelligence" has been around since its introduction at a science conference at Dartmouth University in 1956. But only in the past several years have we started seeing theory put into practice the way those researchers imagined. We now have machines that can translate languages, compose music, write novels, and operate vehicles. So what might the implications of these developments be for educators and students? The primary goal of AI research may be to teach machines how to learn, thereby automating some of the tasks that complicate our everyday lives, but brain scientists are saying it goes both ways: We now know more about human learning as a result of machine learning, and it has some exciting implications for the classroom.

Evolutions in machine learning will require a shake-up of higher education to ensure people have the right skills to get the most out of a world gradually succumbing to automation. That's according to a report by The Royal Society into the various impact machine learning will have on society and business; unsurprisingly, like many reports and hot takes from various bodies and industry, The Royal Society found that the rise of machine learning can bring a host of benefits. But amid the potential to yield smart applications, better services, and extract value from big data being harvested by Internet of Things (IoT) networks, The Royal Society highlighted that smart machines and systems will need new skills to not only keep them up and running but also ensure that robots do not replace human workers completely. "Machine learning will increasingly feature in both our work and personal lives. While not necessarily replacing jobs or functions outright, machine learning will force us to think about our occupations, and the skills necessary to function in a world where these systems are ubiquitous," the report explained.

Aerin Kim, Data Scientist and Founder of resumé checker BYOR (Build Your Own Resume) uses Phrase2Vec NLP parsing technology to help users improve their CV by examining words and phrases and then, using the Deep Learning parser, suggesting how to make it better. Aerin will explain some Deep Learning NLP essentials at next weekend's AI With The Best online conference, a follow-up of her previous talk on Phrase2Vec which you can catch here. We were pleased to have asked Aerin lots of questions last time and happy to see the amazing progress for her startup! You can find out more during her live talk this weekend -- but for now, here are her answers to our burning questions. Q Congratulations on the growth of BYOR labs -- what have you been up to since last September?