The world of startups is constantly moving and evolving. With the exponential growth of deep learning research and technologies in recent years, innovative new companies are often funded, acquired and transformed from startups to industry leaders extremely quickly. MedicSen is a startup focused on developing non-invasive treatments for diabetes, utilising connected devices, machine learning algorithms and a cloud platform to revolutionise diabetic patient's lives. By applying artificial intelligence and sensor tech, the algorithm can predict future glucose levels and risky events and, according to that, give the patient medical advice and instructions for the amount of insulin they need and at what time. Since we first met the MedicSen team at the 2016 Deep Learning in Healthcare Summit in London, their team has grown and their mission has evolved.

The smokestack of a refinery stands next to the Mantaro River in La Oroya, Peru. One of the things that has intrigued me the most about deep learning image cataloging algorithms is their ability to watch the world go by at scale each day through the incredible volume of news and social media images that are generated from every corner of the world and essentially generate a live ground truthed catalog of what's happening moment by moment. Of particular interest for disaster response and environmental monitoring is the ability of such algorithms to recognize imagery of flooding, drought, smog, litter, destruction, violence and other indicators of ongoing ground and air pollution and sudden natural disasters. What might a system look like? Two years ago I met Kadi Kenk, Head of Partnerships for "Let's do it" which is a social good organization founded in Estonia in 2008 that bills itself as a "social movement against trash."

The ubiquitous presence of sequence data across fields such as the web, healthcare, bioinformatics, and text mining has made sequence mining a vital research area. However, sequence mining is particularly challenging because of difficulty in finding (dis)similarity/distance between sequences. This is because a distance measure between sequences is not obvious due to their unstructuredness---arbitrary strings of arbitrary length. Feature representations, such as n-grams, are often used but they either compromise on extracting both short- and long-term sequence patterns or have a high computation. We propose a new function, Sequence Graph Transform (SGT), that extracts the short- and long-term sequence features and embeds them in a finite-dimensional feature space. Importantly, SGT has low computation and can extract any amount of short- to long-term patterns without any increase in the computation, also proved theoretically in this paper. Due to this, SGT yields superior result with significantly higher accuracy and lower computation compared to the existing methods. We show it via several experimentation and SGT's real world application for clustering, classification, search and visualization as examples.

Neural networks were invented in the '60s, but recent boosts in big data and computational power made them actually useful. A new discipline called "deep learning" has arisen that can apply complex neural network architectures to model patterns in data more accurately than ever before. The results are undeniably impressive. Computers can now recognize objects in images and video and transcribe speech to text better than humans can. Google replaced Google Translate's architecture with neural networks, and now machine translation is also closing in on human performance.

AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a Java developer, this book gives you a great opportunity to expand your skillset. Starting with an introduction to basic machine learning algorithms, to give you a solid foundation, Deep Learning with Java takes you further into this vital world of stunning predictive insights and remarkable machine intelligence. Once you've got to grips with the fundamental mathematical principles, you'll start exploring neural networks and identify how to tackle challenges in large networks using advanced algorithms.

Editor's note: This is a followup to the recently published part 1. You may want to check it out before moving forward. In the last article we went through some basics of image-processing using OpenCV and basics of DICOM image. In this article we will talk about basics of deep learning from the lens of Convolutional Neural Nets. In the next article we will use Kaggle's lung cancer data-set, review the key items to look for in a lung cancer DICOM image and use Kera's to develop a model to predict lung cancer.

At this years's F8 conference, Facebook's annual developer event, Facebook announced Caffe2 in collaboration with Nvidia. This framework gives developers yet another tool for building deep learning networks for machine learning. But I am super pumped about this one, because it is specifically designed to operate on mobile devices! So I couldn't resist but start digging in immediately. I'm still learning, but I want to share my journey in working with Caffe2.

Yes, you can run TensorFlow on a $39 Raspberry Pi, and yes, you can run TensorFlow on a GPU powered EC2 node for about $1 per hour. And yes, those options probably make more practical sense than building your own computer. But if you're like me, you're dying to build your own fast deep learning machine. OK, a thousand bucks is way too much to spend on a DIY project, but once you have your machine set up, you can build hundreds of deep learning applications, from augmented robot brains to art projects (or at least, that's how I justify it to myself). At the very least, this setup will easily outperform a $2,800 Macbook Pro on every metric other than power consumption and, because it's easily upgraded, stay ahead of it for a few years to come.

A missile fired by Iraqi government forces. Last week I wrote about how deep learning image recognition algorithms offer a potential solution to the epidemic of violent imagery cascading across the online world, especially with respect to the rising issue of live streaming violent acts that can reach vast audiences before any human reviewer has a chance to examine the footage. What might this actually look like in practice? For the past year my open data GDELT Project has been applying Google's Cloud Vision API (its cloud-based deep learning image cataloging service) to global news imagery from every country in the world, totaling more than a quarter billion images to date. While no algorithm is perfect and the Vision API does make mistakes, it has proven remarkably adept at recognizing an incredible wealth of violence-related indicators from imagery spanning the entire globe.