Open Bionics, a startup which is coming up with next gen bionic limbs, leverages 3D printing, sensors and machine learning to create the "Hero Arm", and provides superpowers to five million upper limb amputees globally!

Since I was in high school, I've had this weird obsession of squeeze the key concepts of everything that I learn in one page. Looking back, that was probably my lazy mind's way to get away with the least amount of required work to pass an exam…but interestingly that abstraction effort also helped a lot to learn those concepts in a deeper level and to remember them longer. Nowadays when I teach Machine Learning, I try to teach it in two parallel tracks: a) main concepts and b) methods and theoretical details, and make sure my students can look at each new method through the lens of the same concepts. Recently I got a chance to read "Machine Learning Yearning" by Andrew Ng, which seemed to be his version of abstracting some of the practical ML concepts without getting into any formula or implementation details. While they can see so simple and obvious, as an ML engineer I can attest that losing sight of those simple tips are among the most common causes for an ML research to fail in production, and being mindful of them is what distinguishes a good data science work from a mediocre one.

Description: This book provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The authors present the material in an accessible style and motivate concepts using real-world examples. Be prepared, it is a big book!. Also, check out their great probability cheat sheet here.

AI is transforming numerous industries. Machine Learning Yearning, a free ebook from Andrew Ng, teaches you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work.

Top Stories, Tweets Top Stories, Aug 5-11: Knowing Your Neighbours: Machine Learning on Graphs; What is Benford's Law and why is it important for data science? Top KDnuggets tweets, Jul 31 - Aug 06: NLP vs. NLU: from Understanding a Language to Its Processing News Exploratory Data Analysis Using Python Meetings The slow, startling triumph of Reverend Bayes - John Elder's 2019 Keynote at PAW in London Cambridge Analytica whistleblower Chris Wylie to headline Big Data LDN 2019 keynote programme Academic Postdoctoral position (2 years) in multivariate analysis and deep learning PhD student position in computational science with focus on chemistry Monash University: Research Fellow - Computer Vision [Melbourne, Australia] Image of the week 12 NLP Researchers, Practitioners, Innovators to Follow Learn how to do Machine Learning on Graphs; Follow these 12 amazing leaders in NLP; Read the explanation of Deep Learning for NLP, including ANNs, RNNs and LSTMs; Understand what is Benford's Law and why is it important for data science; Find the 6 key concepts in Andrew NG Machine Learning Yearning; and more. Knowing Your Neighbours: Machine Learning on Graphs 12 NLP Researchers, Practitioners & Innovators You Should Be Following Deep Learning for NLP: ANNs, RNNs and LSTMs explained! What is Benford's Law and why is it important for data science?

Here are the most popular posts in KDnuggets in August, based on the number of unique page views (UPV), and social share counts from Facebook, Twitter, and Addthis. Most Shareable (Viral) Blogs Among the top blogs, here are the blogs with the highest ratio of shares/unique views, which suggests that people who read it really liked it.

If you are diving into AI and Machine Learning, Andrew Ng's book is a great place to start. Learn about six important concepts covered to better understand how to use these tools from one of the field's best practitioners and teachers. Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them.

Machine Learning Yearning is about structuring the development of machine learning projects. The book contains practical insights that are difficult to find somewhere else, in a format that is easy to share with teammates and collaborators. Most technical AI courses will explain to you how the different ML algorithms work under the hood, but this book teaches you how to actually use them. If you aspire to be a technical leader in AI, this book will help you on your way. Historically, the only way to learn how to make strategic decisions about AI projects was to participate in a graduate program or to gain experience working at a company.

Welcome to the first edition of Memeified AI! Memeifieid AI summarizes important works in the AI field as memes and tweets, so that busy people can get a sense of what's inside. The titles shown in bold are straight from Andrew's book. The memes and tweetish summaries are mine, so any errors are clearly me just thinking unclearly. For the curious, I also provide a 20-minute talk with Q&A if your group is into that sort of thing. Before we get started, I thought I'd share a personal story about how these memefieid posts came about, so you can understand the source of my madness.

This is a book I am writing over the next few months. AI, Machine Learning and Deep Learning are transforming numerous industries. I have been writing a book, Machine Learning Yearning, to teach you how to structure Machine Learning projects. This book is focused not on teaching you ML algorithms, but on how to make ML algorithms work. Some technical AI classes will give you a hammer; this book teaches you how to use the hammer.