This video is a recap of our June 2019 EMEA TWiML Online Meetup: MeshCNN: A Network with an Edge. In this month's community segment, we discuss the Imagenet-trained CNNs are biased towards texture article and the Open-sourcing Ax and Bo Torch article. In our presentation segment, Rana Hanocka presents on a method for employing neural networks on irregular triangular meshes with the MeshCNN: A Network with an Edge paper by Rana Hanocka et al. For links to the papers, podcasts, and more mentioned above or during this meetup, for more information on previous meetups, or to get registered for upcoming meetups, visit twimlai.com/meetup!

In this episode I'm joined by Alexander Chukovski, Director of Data Services at Munich, Germany based career platform, Experteer. In this podcast we explore Alex's journey to implement machine learning at Experteer. Alex and I discuss the Experteer NLP pipeline and how it's evolved over time to address the company's need for greater automation in the way it processes jobs on its platform. We also discuss Alex's work with deep learning based ML models, including models like VDCNN and Facebook's FastText offering, which he's particularly excited about. Finally, we briefly discuss recent papers that look at transfer learning for NLP, how Alex keeps up with recent academic papers, and a few tips for people looking to inject ML/DL in their products or projects.

In this episode, I'm joined by Nathan Kutz, Professor of applied mathematics, electrical engineering and physics at the University of Washington. Nathan and I met a few months ago at the Prepare.AI conference in St. Louis where he gave a talk on "Machine Learning to Discover Physics and Engineering Principles." Our conversation is laser-focused on his research into the use of machine learning to help discover the fundamental governing equations for physical and engineering systems from time series measurements. We explore the application of his work to self-tuning fiber-optic lasers as well as to biological systems and other complex multi-scale systems. On July 17th at 5pm PT, Nic Teague will lead a discussion on the paper Quantum Machine Learning by Jacob Biamonte et al, which explores how to devise and implement concrete quantum software for accomplishing machine learning tasks.

This week on the podcast we're running a series of shows consisting of conversations with some of the impressive speakers from an event called the AI Summit in New York City. The theme of the conference, and the series, is AI in the Enterprise, and I think you'll find it really interesting in that it includes a mix of both technical and case-study-oriented discussions. My guest for this first show in the series is, Hillery Hunter, IBM Fellow & Director of the Accelerated Cognitive Infrastructure group at IBM's T.J. Watson Research Center. Hillery and I met a few weeks back in New York and I'm really glad that we were able to get her on the show. Hillery joins us to discuss her team's research into distributed deep learning, which was recently released as the PowerAI Distributed Deep Learning Communication Library or DDL.