GANs for Good

Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating realistic image, video, and voice outputs. Rooted in game theory, GANs have wide-spread application: from improving cybersecurity by fighting against adversarial attacks and anonymizing data to preserve privacy to generating state-of-the-art images, colorizing black and white images, increasing image resolution, creating avatars, turning 2D images to 3D, and more.

At DeepLearning.AI, our mission is to make world-class AI education accessible to everyone. Created by experts from the industry and academic, our brand new GANs Specialization will equip you with foundational knowledge and hands-on training to build cutting-edge GANs systems. Enroll NOW!

To celebrate the launch of GANs Specialization, we’ve assembled a panel of GANs experts. They will discuss some of their current projects and the importance and future of GANs and also provide practical career advice for ML practitioners.

Please check the Event Agenda below:

Agenda: PDT (*subject to change)

MC: Ryan Keenan, Director of Product at DeepLearning.AI

• 10:00am-10:05am: Event introduction
• 10:05am-10:15am: Opening Keynote: Ian Goodfellow, Director of Machine Learning in the Special Projects Group, Apple
• 10:15am-10:25am: Keynote: Role of interaction and disentanglement in training GANs: Animashree Anandkumar, Director of machine learning research, NVIDIA
• 10:25am-11:05am: Panel discussion

• Animashree Anandkumar, Director of machine learning research, NVIDIA
• Alexei Efros, Professor at EECS Department at UC Berkeley
• Ian Goodfellow, Director of Machine Learning in the Special Projects Group, Apple
• Andrew Ng, Founder of DeepLearning.AI
• Sharon Zhou, Computer Science, Stanford; Course Instructor, GANs Specialization

• 11:05am-11:20am: Q&A
• 11:20am-11:30am: GANs Specialization course demo by Sharon Zhou, Computer Science, Stanford; Course Instructor, GANs Specialization

This is an online-only event. We will email you a YouTube Live URL two days before the event. For anyone registered, we will be sending out the recorded sessions if you can’t attend live.

Expected audience AI knowledge level:

Beginner, Intermediate

Course Promo Code

After the event, we will provide a limited number of promotional codes for attendees who complete a post-event survey sent by DeepLearning.AI. With this promo code, learners can avail 50% off the first month subscription for any deeplearning.ai course on Coursera. You can check out a full list of courses here.

About guest speakers:

Animashree Anandkumar: Anima Anandkumar joined NVIDIA as director of machine learning research in 2018. She is also a Bren professor at Caltech CMS department since 2017. Her research spans both theoretical and practical aspects of machine learning. In particular, she has spearheaded research in tensor-algebraic methods, large-scale learning, deep learning, probabilistic models, and non-convex optimization.

Anima is the recipient of several awards such as the Alfred. P. Sloan Fellowship, NSF Career Award, Young investigator awards from the Air Force and Army research offices, Faculty fellowships from Microsoft, Google and Adobe, and several best paper awards. She is the youngest named professor at Caltech, the highest honor bestowed to an individual faculty. She is part of the World Economic Forum's Expert Network consisting of leading experts from academia, business, government, and the media. She has been featured in documentaries by PBS, KPCC, wired magazine, and in articles by MIT Technology review, Forbes, Yourstory, O’Reilly media, and so on.

Anima received her B.Tech in Electrical Engineering from IIT Madras in 2004 and her PhD from Cornell University in 2009. She was a postdoctoral researcher at MIT from 2009 to 2010, visiting researcher at Microsoft Research New England in 2012 and 2014, assistant professor at U.C. Irvine between 2010 and 2016, associate professor at U.C. Irvine between 2016 and 2017, and principal scientist at Amazon Web Services between 2016 and 2018.

Alexei Efros: Alexei is a professor at EECS Department at UC Berkeley, where he is part of the Berkeley Artificial Intelligence Research Lab (BAIR). Before that, he spent nine years on the faculty of the Robotics Institute at CMU. He is also still remembered in lovely Oxford, where he did a post-doc with Andrew Zisserman. Starting in 2007, he has also been closely collaborating with Team WILLOW at École Normale Supérieure / INRIA in beautiful Paris. The central goal of his research is to use vast amounts of unlabelled visual data to understand, model, and recreate the visual world around us. His research has been mainly in data-driven computer vision, as well as its projection onto computer graphics and computational photography. In the last five years, his lab has been at the forefront of reviving self-supervised learning. His other interests include human vision, visual data mining, robotics, and the applications of computer vision to the visual arts and the humanities. He also enjoys making scientific bets.

Ian Goodfellow: Ian Goodfellow is a researcher working in machine learning, currently employed at Apple Inc. as its Director of Machine Learning in the Special Projects Group. He was previously employed as a research scientist at Google Brain. He is the Lead Author of the first major textbook on deep learning—Deep Learning (MIT Press).He invented Generative Adversarial Networks (GANs) and with his 2014 paper is responsible for launching the incredible growth of research on GANs. He got his BS and MS at Stanford, his PhD at University of Montreal with Yoshua Bengio and Aaron Courville.

Andrew Ng: Andrew Ng is Founder of DeepLearning.AI, Founder and CEO of Landing AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University.As a pioneer both in machine learning and online education, Dr. Ng has changed countless lives through his work and research in the field of artificial intelligence. He now focuses his time primarily on his entrepreneurial ventures, looking for the best ways to accelerate responsible AI practices in the larger global economy.

Sharon Zhou: Sharon is a CS PhD candidate at Stanford University, advised by Andrew Ng. Sharon Zhou’s work in AI spans from theoretical to applied, in medicine, climate, and more broadly, social good. Previously a machine learning product manager at Google and various startups, Sharon is a Harvard graduate in CS and Classics. She likes humans more than AI, though GANs occupy a special place in her heart.

About GANs Specialization:

The DeepLearning.AI Generative Adversarial Networks (GANs) Specialization provides an exciting introduction to image generation with GANs, charting a path from foundational concepts to advanced techniques through an easy-to-understand approach. It also covers social implications, including bias in ML and the ways to detect it, privacy preservation, and more.

After completing this Specialization, you will have learned how to achieve the state-of-the-art in realistic generation. You will be able to generate realistic images, edit those images by controlling the output in a number of ways (eg. convert a horse to a zebra or lengthen your hair or make yourself older), quantitatively compare generators, convert an image to another (eg. turning a sketch into a photo-realistic version), animate still images, solve many of the challenges that GANs are notorious for, and more.