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
This course introduces you to deep learning: the state-of-the-art approach to building artificial intelligence algorithms. We cover the basic components of deep learning, what it means, how it works, and develop code necessary to build various algorithms such as deep convolutional networks, variational autoencoders, generative adversarial networks, and recurrent neural networks. A major focus of this course will be to not only understand how to build the necessary components of these algorithms, but also how to apply them for exploring creative applications. We'll see how to train a computer to recognize objects in an image and use this knowledge to drive new and interesting behaviors, from understanding the similarities and differences in large datasets and using them to self-organize, to understanding how to infinitely generate entirely new content or match the aesthetics or contents of another image. Deep learning offers enormous potential for creative applications and in this course we interrogate what's possible. Through practical applications and guided homework assignments, you'll be expected to create datasets, develop and train neural networks, explore your own media collections using existing state-of-the-art deep nets, synthesize new content from generative algorithms, and understand deep learning's potential for creating entirely new aesthetics and new ways of interacting with large amounts of data.
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Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.
Ability to construct a TensorFlow graph for generative or discriminative modeling
Ability to to visualize and interrogate deep representations of a deep neural network
Ability to synthesize and explore manifolds of generative models
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. For students less familiar with numpy/matplotlib though still confident with python, a rough guide is provided for the 1st session as a jupyter notebook.
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