This first course in the two-part program, Creative Applications of Deep Learning with TensorFlow, 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.









What students are saying:





"It was a fantastic course and I want to say "super big thank you" to the instructor Parag Mital. His lectures were highly valuable and inspiring. It not only gave me inspiration in how to apply ML for art, it gave me deeper insights in Deep Learning in general."









"After taking several courses in Machine Learning, I came across this course and it immediately caught my attention due to the the speed of delivery, content topics and it's pace when talking about concepts such as gradient descent and convolutions. Honestly, the course truly is EXCELLENT. Parag really is great a presenting the materials in an easy-to-understand manner, and perhaps more importantly, he has you focus on the RIGHT concepts and not going down rabbit-holes."

