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 our existing background in Deep Learning to state of the art techniques in audio, image and text modeling. We'll see how dilated convolutions can be used to model long term temporal dependencies efficiently using a model called WaveNet. We'll also see how to inspect the representations in deep networks using a deep generator network, leading to some of the strongest insights into deep networks and the representations they learn. We'll then switch gears to one of the most exciting directions in Deep Learning thus far: Reinforcement Learning. We'll take a brief tour of this fascinating topic and explore toolkits released by OpenAI, DeepMind, and Microsoft. Finally, we're teaming up with Google Brain's Magenta Lab for our last session on Music and Art Generation. We'll explore Magenta's libraries using RNNs and Reinforcement Learning to create generative and improvised music.
"This course lets you explore things like audio synthesis, music generation and natural language processing using the Tensorflow skills learned in the previous two courses. It is very open-ended. Parag and the Google Magenta team give some great overviews and then set you free to explore each space further. Highly recommended!"
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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.
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.
If a student signs up for the Creative Applications of Deep Learning program, it is recommended that these courses are taken sequentially.
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