Please go to London Artificial Intelligence Deep Learning Meet-Up page to register.
– 6:00 to 6:30pm Pizza and Drinks (please come early if possible)
– Introduction by H2O.ai (5 mins)
– Introduction by Yoox-Net-A-Porter Cognitive Commerce Team (15 mins)
– Explainable Artificial Intelligence (XAI) by Torgyn Shaikhina (20 mins)
– Neural Style Transfer by Ambroise Laurent (20 mins)
– Applying AI/ML to e-Learning by Shabbir Mookhtiar (20 mins)
– (if time allows) The journey to a real Moneyball app by Joe (20 mins)
Neural style transfer, or how to be Picasso when you don’t know how to paint by Ambroise Laurent
Take a dive in the world of deep learning as it applies to art and images – We will be looking at neural style transfer, a deep learning technique to create artistic imagery by separating and combining the style and content of images. In this talk you will see neural nets demystified, the history behind the networks we have today and learn a few tips that you can apply to your own deep learning projects. We will also cover the challenges facing visual tasks and what different companies are experimenting with.
I am a developer at Theodo, a London / Paris based startup that helps large corporates and startups alike, solve complex business problems through cutting edge digital solutions. An interest for technology and coding has pushed me to pick up deep learning as a hobby and I look forward to sharing with you, the insights I have gathered in my personal projects and elsewhere.
Applying AI/ML to e-Learning
In today’s Digital era, capability building and knowledge retention in an organisation has changed. 22% Millennial’s now identify continuous Training and Development as a key aspect for their growth and retention in the organisation. Among the wider demographic as well, people have varied ways of learning. Some prefer reading, others watching videos and yet others who prefer audio based podcasts etc. What is the best way to target this wide audience of keen learners and personalise the experience to make e-learning easily accessible and much more immersive and interesting? In this session about applying AI/ML to learning, we will look at how to tackle this problem and take learning into the next generation.
Shabbir Mookhtiar is a product evangelist at Tridat. He has worked in the media technology sector for 10+ years and is now moving into the exciting new world of AI/ML
Explainable Artificial Intelligence by Torgyn
Making Multimillion-dollar Baseball Decisions with H2O AutoML and Shiny by Joe Chow
Joe recently teamed up with IBM and Aginity to create a proof of concept “Moneyball” app for the IBM Think conference in Vegas. The original goal was to prove that different tools (e.g. H2O, Aginity AMP, IBM Data Science Experience, R and Shiny) could work together seamlessly for common business use-cases. Little did Joe know, the app would be used by Ari Kaplan (the real “Moneyball” guy) to validate the future performance of some baseball players. Ari recommended one player to a Major League Baseball team. The player was signed the next day with a multimillion-dollar contract. This talk is about Joe’s journey to a real “Moneyball” application.
Jo-fai (or Joe) Chow works at H2O.ai as a data science evangelist / community manager.