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.

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?

In this episode, Jack Rasiel speaks with Kostas Daniilidis, Professor of Computer and Information at the University of Pennsylvania, about new developments in computer vision and robotics. Daniilidis' research team is pioneering new approaches to understanding the 3D structure of the world from simple and ubiquitous 2D images. They are also investigating how these techniques can be used to improve robots' ability to understand and manipulate objects in their environment. Daniilidis puts this in the context of current trends in robot learning and perception, and speculates how it will help bring more robots from the lab to the "real world". How does bleeding edge research become a viable product? Daniilidis speaks to this from personal experience, as an advisor to startups spun out from the GRASP Lab and Penn's Pennovation incubator. Kostas Daniilidis is the Ruth Yalom Stone Professor of Computer and Information Science at the University of Pennsylvania where he has been faculty since 1998.

Twenty-three years after his death, former Formula 1 world champion Ayrton Senna's name is almost as valuable as when he was alive - and it is making a difference in his home country of Brazil. It is Friday afternoon and children around the age of 12 are gathered in the computer lab of a public school in Itatiba, a small town an hour away from Sao Paulo. Class time is already over for the week, but these students have chosen to stay in school for extracurricular activities. They are learning Scratch, a piece of software developed by MIT experts that aims to teach kids how to code. Most public schools in Brazil don't have computer coding in their curriculum.

Nutrient-based meal recommendations have the potential to help individuals prevent or manage conditions such as diabetes and obesity. However, learning people's food preferences and making recommendations that simultaneously appeal to their palate and satisfy nutritional expectations are challenging. Existing approaches either only learn high-level preferences or require a prolonged learning period. We propose Yum-me, a personalized nutrient-based meal recommender system designed to meet individuals' nutritional expectations, dietary restrictions, and fine-grained food preferences. Yum-me enables a simple and accurate food preference profiling procedure via a visual quiz-based user interface, and projects the learned profile into the domain of nutritionally appropriate food options to find ones that will appeal to the user. We present the design and implementation of Yum-me, and further describe and evaluate two innovative contributions. The first contriution is an open source state-of-the-art food image analysis model, named FoodDist. We demonstrate FoodDist's superior performance through careful benchmarking and discuss its applicability across a wide array of dietary applications. The second contribution is a novel online learning framework that learns food preference from item-wise and pairwise image comparisons. We evaluate the framework in a field study of 227 anonymous users and demonstrate that it outperforms other baselines by a significant margin. We further conducted an end-to-end validation of the feasibility and effectiveness of Yum-me through a 60-person user study, in which Yum-me improves the recommendation acceptance rate by 42.63%.

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.