I listened carefully to Yann Fleureau's speech during the DATADRIVENPARIS event about his 4 past years as a Co-Founder and CEO of CardioLogs and his journey towards building and selling an AI-based Clinical Decision Support System (CDSS) for Clinicians in the Cardiology space. CardioLogs is a Paris-based Startup building Deep-Learning Algorithms for ECG (EKG) analysis. They have raised approximately 10M$ to date and have won approval for commercialization in Europe of the first medical grade deep-learning technology in 2016 and the second in the US in 2017. Yann is a graduate from the prestigious Polytechnic School of Paris (X) and passionate about New Technology & Medicine (https://cardiologs.com/). The last 4 years of CardioLogs illustrate well the challenges of implementing an AI-based solution in clinical practice.

NVIDIA's GPU Technology Conference is the premier event on artificial intelligence. Connect with experts to get hands-on technical training and insights into the latest AI and data science approaches, applications and breakthroughs. Choose from 100 talks, panels, posters and demos covering deep learning, machine learning, cybersecurity, autonomous machines, HPC, intelligent video analytics, healthcare, 5G, VR and more.

Written by Dr. Kirk Borne We often think of analytics on large scales, particularly in the context of large data sets ("Big Data"). However, there is a growing analytics sector that is focused on the smallest scale. That is the scale of digital sensors -- driving us into the new era of sensor analytics. Small scale (i.e., micro scale) is nothing new in the digital realm. After all, the digital world came into existence as a direct consequence of microelectronics and microcircuits.

Google will receive five years' worth of NHS patients' sensitive records under the terms of a deal signed last month, despite controversy over similar contracts in the past. The extent to which patient data has been shared between an NHS trust in England and AI company DeepMind was first revealed by New Scientist in 2016 and later ruled that it failed to comply with the law by the data watchdog for failures over informing patients.

An effective deep learning development process is critical for widespread industrial adoption, particularly in the automotive sector. A typical industrial deep learning development cycle involves customizing and re-designing an off-the-shelf network architecture to meet the operational requirements of the target application, leading to considerable trial and error work by a machine learning practitioner. This approach greatly impedes development with a long turnaround time and the unsatisfactory quality of the created models. As a result, a development platform that can aid engineers in greatly accelerating the design and production of compact, optimized deep neural networks is highly desirable. In this joint industrial case study, we study the efficacy of the GenSynth AI-assisted AI design platform for accelerating the design of custom, optimized deep neural networks for autonomous driving through human-machine collaborative design.

It's nearly impossible to keep up with all the latest amazing research that's happening all around the globe. From architecture optimization to task-based research and beyond, there are so many incredible efforts being undertaken to push the ML landscape into new, exciting frontiers. And while we can't possibly cover every new development, we have a number of excellent Heartbeat articles that review, summarize, and otherwise explore current research trends. This list should provide a good starting point for diving into some of the core ML research out there.

Programmers and developers can tap into a variety of languages to build applications, websites, and other programs. Ultimately, your preferred language is going to be the one with which you're most comfortable and that gets the job done most effectively. But one particular language emerged as the top choice in a study released in September by the IEEE, and that's Python. To gauge the popularity of different languages, the IEEE's study ranked 11 data points from eight different sources: CareerBuilder, Google, GitHub, Hacker News, the IEEE, Reddit, Stack Overflow, and Twitter. Depending on the source, the IEEE looked at such factors as searches, new repositories, posts mentioning each language, and job postings made within the last 30 days.

I made an implementation of MixMatch (paper) in PyTorch, thought I'd share for those who are interested. Works as an installable package which you can use to create a dataloader that implements the mixmatch algorithm, as well as construct the appropriate loss function. Feedback and comments are appreciated!

In this tutorial, you will learn how to use Cyclical Learning Rates (CLR) and Keras to train your own neural networks. Using Cyclical Learning Rates you can dramatically reduce the number of experiments required to tune and find an optimal learning rate for your model. Last week we discussed the concept of learning rate schedules and how we can decay and decrease our learning rate over time according to a set function (i.e., linear, polynomial, or step decrease). Cyclical Learning Rates take a different approach. In practice, using Cyclical Learning Rates leads to faster convergence and with fewer experiments/hyperparameter updates.

So much of what Engineers design and build today takes inspiration from nature. Boeing added flaps on the wings of their planes mimicking Eagles, the shape of Whale fins helped reduce drag on wind turbines and the noses of bullet trains look suspiciously like a Kingfisher's beak. Nature has often already found elegant solutions to problems that our best and brightest work on every day. Nature finds these solutions through a process of natural selection where the genes of the best performing organisms are passed on to successive generations. "Can we use Evolution to autonomously design and build Machine Learning algorithms?" Evolutionary Machine Learning uses Darwinian natural selection to autonomously design, train and optimise Neural Networks.