Enrollment for this course has closed. But you can enroll in a future offering (please select)
Not available for purchase in India
Enrollment for this course has closed. But you can enroll in a future offering (please select)
Not available for purchase in India
Filmed with exclusive content featuring Google Magenta
TensorFlow logo and any related marks are trademarks of Google Inc.
This course extends the material from the first course on Creative Applications of Deep Learning, providing an updated landscape on the state of the art techniques in recurrent neural networks. We begin by recapping what we've done up until now and show how to extend our practice to the cloud where we can make use of much better hardware including state-of-the- art GPU clusters. We'll also see how the models we train can be deployed for production environments. The techniques learned here will give us a much stronger basis for developing even more advanced algorithms in the final course of the program. We then move on to some state-of-the-art developments in Deep Learning, including adding recurrent networks to a variational autoencoder in order to learn where to look and write. We also look at how to use neural networks to model parameterized distributions using a mixture density network. Finally, we look at a recent development in Generative Adversarial Networks capable of learning how to translate unpaired image collections so that each collection looks like the other one. Along the way, we develop a firm understanding in theory and code about some of the components in each of these architectures that make them possible.
Verify Your Achievements
Whenever you complete a course as a premium member, you can earn a verified Certificate of Accomplishment. These certificates are proof that you completed an online course on our platform.
Easily Shareable
Using its unique link, you can share your certificate with everyone from future employers and schools, to friends, family, and colleagues. It's the perfect tool to help you land that new job or promotion, apply to college, or simply share your achievements with the world.
Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.
Parag K. MITAL (US) is an artist and interdisciplinary researcher obsessed with the nature of information, representation, and attention. Using film, eye-tracking, EEG, and fMRI recordings, he has worked on computational models of audiovisual perception from the perspective of both robots and humans, often revealing the disjunct between the two, through generative film experiences, augmented reality hallucinations, and expressive control of large audiovisual corpora. Through this process, he balances his scientific and arts practice, with both reflecting on each other: the science driving the theories, and the artwork re-defining the questions asked within the research. His work has been exhibited internationally including the Prix Ars Electronica, ACM Multimedia, Victoria & Albert Museum, London’s Science Museum, Oberhausen Short Film Festival, and the British Film Institute, and featured in FastCompany, BBC, NYTimes, CreativeApplications.Net, and CreateDigitalMotion.
A short guide is provided here: https://github.com/pkmital/CADL to help with the installation of each of these components:
There is also an introductory session for those less familiar with python: https://github.com/pkmital/CADL/blob/master/session-0/session-0.ipynb
Some knowledge of basic python programming is assumed, including how to start a python session, working with jupyter (ipython) notebook (for homework submissions), numpy basics including how to manipulate arrays and images, how to draw images with matplotlib, and how to work with files using the os package. You should also have completed the first course in the CADL program before taking this second course.
Peer Assessment Code of Conduct: Part of what makes Kadenze a great place to learn is our community of students. While you are completing your Peer Assessments, we ask that you help us maintain the quality of our community. Please:
Please understand that posts which violate this Code of Conduct harm our community and may be deleted or made invisible to other students by course moderators. Students who repeatedly break these rules may be removed from the course and/or may lose access to Kadenze.
Students with Disabilities: Students who have documented disabilities and who want to request accommodations should refer to the student help article via the Kadenze support center. Kadenze is committed to making sure that our site is accessible to everyone. Configure your accessibility settings in your Kadenze Account Settings.
