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Emmanuel gives a step by step tutorial how to build a semantic search engine for text and images, with some coding example. This approach presented extend naturally to other applications such as image and video captioning, reading text from videos, selecting optimal thumbnails and generating code from sketches of websites and so on. There's sort of expressive models in both computer vision and NLP but now it is the time to  bring those two worlds together and search for images using text and vice versa.Emmanuel is a Machine Learning Engineer at Stripe, and previous Head of AI at Insight Data Science. He has years of experience going from product ideation to effective implementations. At Insight, he has led over a hundred AI projects from ideation to finished product in a variety of domains including Computer Vision, Natural Language Processing, and Speech Processing.

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Eric Tramel discusses the basic concepts underlying the federated Machine Learning approach, the advantages it brings, as well as the challenges associated with constructing federated solutions. Federated learning could be an interface between data science, machine learning, engineering, DevOps, software data, and security engineering, and bringing all this together in one spot.For machine learning test, the compute is happening where the data is, so now we have some other extra complications, we need to bring the compute to the data.If it's data that's generated in a hospital, never take it out of the hospital, leave it there.Eric is the head of Owkin’s Federated Learning Research group, where he heads a team of researchers and engineers to study and build applications of federated and privacy-aware machine learning techniques to medical data.

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François Chollet is the main creator of the Keras framework. Probably everyone already knows the Keras, but just for sure Keras is a leading deep learning framework for Python, with over 350,000 users and over 700 open-source contributors .Keras is an easy-to-use machine learning library that can be used for doing fast prototyping to building complex custom models for research and production.Keras has the goal to make deep learning accessible to everyone. Now let’s see how to integrate keras and tensorflow in a deeper level. It’s time to learn from it’s creator.François is Staff Software engineer at Google and a brilliant AI researcher whose main interest is understanding the nature of abstraction and developing algorithms capable of autonomous abstraction (i.e. general intelligence)  and democratizing the development and deployment of AI technology by making it easier to use and explaining it clearly.

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Analysis and synthesis of human motion is a challenging task in computer vision and graphics. The emerging power of deep learning tools paves the way to a fascinating research path the enable intuitive motion editing in videos and manipulation of motion of 3D characters. Have you ever been amazed by the quality and realism of characters motion in movies and video games? Do you wonder how motion can be captured and modeled? How deep learning and data-driven approaches may assist with that? You can find the answers to these questions in Kfir's talk.

Kfir Aberman is a research scientist at the Reality Capture Group in the Beijing Film Academy, where he creates innovative solutions to core technological problem related to the film industry. Kfir received his Ph.D. from Tel-Aviv University where he worked with Prof. Daniel Cohen-Or on various topics related to computer graphics. Kfir has extensive experience in developing and training deep networks for synthesis of visual content such as images, videos, and 3D characters.

About

I'm a machine learner, data scientist, ex-roboticist, and consultant.
I like turning crazy ideas into production systems using data science and software engineering.