- Getting started with TensorFlow 2.0, Josh Gordon, Developer Advocate at Google AI
- Current capabilties, limitations and future directions of deep learning, François Chollet, Creator of Keras (March 2019)
- Deep Neural Networks for Video Applications, Alex Conway, NumberBoost (March 2019)
- Beyond Supervised Driving by Adrien Gaidon, Toyota Research Institute (March 2019)
-- TALK -- TensorFlow 2.0 is all about ease of use. In this 45 minute talk, I'll cover best practices for beginners and experts, and point you to the latest code examples you can try for each. I'll cover the Keras Sequential, Functional, and Subclassing APIs, as well as built-in training loops, and how to write a custom training loop using a GradientTape. To wrap it up, I'll give a quick summary of a few announcements and updates from the TensorFlow Developer Summit.
-- SPEAKER BIO -- Josh Gordon is a Developer Advocate at Google AI, and also teaches Applied Deep Learning at Columbia University, and Machine Learning at Pace University. He has over a decade of machine learning experience to share. You can find him on Twitter at @random_forests.
Current capabilties, limitations and future directions of deep learning, François Chollet, Creator of Keras
-- TALK -- Deep learning has had amazing successes in recent years. But can it lead to Strong AI? The goal of this talk is to zoom out a little bit -- to look at how deep learning really works, to look at its current limits, and to try to see the road ahead for AI.
-- SPEAKER BIO -- François Chollet is the Creator of Keras (keras.io), a leading deep learning API, and author of the textbook "Deep Learning with Python". He is also a machine learning researcher at Google Brain and a contributor to the TensorFlow machine learning platform.
Slides https://github.com/Machine-Learning-Tokyo/MLT_Talks/blob/master/slides/Francois_Chollet.pdf
-- TALK -- Most CCTV video cameras exist as a sort of time machine for insurance purposes. Deep neural networks make it easy to convert video into data which can then be used to trigger real-time anomaly alerts and optimize complex business processes. Deep learning can also be used in academic research to speed up labeling of video recorded from the point of view of animals wearing go-pros. This talk will present some theory of deep neural networks for video applications as well as academic research and several applied real-world industrial examples.
-- SPEAKER BIO -- Alex is the Founder and Head of Data Science at NumberBoost, a startup based in Cape Town that builds custom A.I. solutions focused on real-time computer vision using deep learning, edge computing and privacy-preserving federated machine learning. NumberBoost has won startup competitions with MultiChoice, Mercedes-Benz, Lloyd's Register in London and NTT Data Japan. He organizes the Cape Town Machine Learning and Deep Learning Meetup groups and chaired the organizing committee for the 2018 Deep Learning IndabaX conference in Cape Town.
-- SLIDES --
-- TALK -- Crowd-sourced steering does not sound as appealing as automated driving. We need to go beyond supervised learning for automated driving, including for computer vision problems seeing great progress with strong supervision today. First, we will motivate why this is required for long-term large-scale autonomous robots. Second, we will discuss recent state-of-the-art results obtained in the ML team at Toyota Research Institute (TRI) for unsupervised domain adaptation from simulation and self-supervised depth and pose prediction from monocular imagery. Finally, I will talk about how we actually scale to large datasets using our cloud infrastructure and distributed deep learning.
-- SPEAKER BIO -- Adrien Gaidon is the Manager of the Machine Learning team and a Senior Research Scientist at the Toyota Research Institute (TRI) in Los Altos, CA, USA, working on open problems in world-scale learning for autonomous driving. He received his PhD from Microsoft Research - Inria Paris in 2012 and has over a decade of experience in academic and industrial Computer Vision, with over 30 publications, top entries in international Computer Vision competitions, multiple best reviewer awards, international press coverage for his work on Deep Learning with simulation, and was a guest editor for the International Journal of Computer Vision. You can find him on LinkedIn (https://www.linkedin.com/in/adrien-gaidon-63ab2358/) and Twitter (www.twitter.com/adnothing).
