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4 Sessions
Program ($500 USD)
Kadenze Academy
Creative Computing, Computational Graphics
Skill Level
Video Transcripts
Generative audio, deep generative networks, generative adversarial networks, sketch to photo, neural doodle, style net
Course Sponsor

Filmed with exclusive content featuring Google Magenta

TensorFlow logo and any related marks are trademarks of Google Inc.

Course Description

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 is in scheduled mode and is open for enrollment. Learn more about scheduled courses here.

Session 1: Modeling Music and Art: Google Brain’s Magenta Lab
We're teaming up with the Google Brain lab, Magenta to explore the generative creation of Music and Art! We'll explore their libraries which use RNNs and Reinforcement Learning to compose, generate, improvise, and even create duets of music.
Session 2: Modeling Language: Natural Language Processing
This session develops an understanding in natural language processing covering word2vec, glove, seq2seq and attention mechanisms.
Session 3: Autoregressive Image Modeling w/ PixelCNN
This session covers an advanced technique for synthesizing objects resembling deep dream techniques. We show how this can be used to much more clearly understand the representations in deep networks.
Session 4: Modeling Audio w/ Wavenet and NSynth
This session covers new work in generative modeling of images, sound, and text using masked and dilated convolution operations. We describe what these are and how they can be used to model various media types very efficiently.
Learning Outcomes

Below you will find an overview of the Learning Outcomes you will achieve as you complete this course.

Whenever you complete a course as a premium member, you can earn a verified Certificate of Accomplishment .

This course is also part of the Program: Creative Applications of Deep Learning with TensorFlow. Earn a verified Specialist Certificate for successfully completing a Program.

These certificates are proof that you completed an online course on our platform and can easily be shared with its unique link.

Credit Elligible Program
This course is one of 3 courses in the Creative Applications of Deep Learning with TensorFlow Program and is offered for credits from Kadenze Academy.

Earn a verified Specialist Certificate after successfully completing a Program. And whenever you complete a course as a Premium member, you earn a verified Certificate of Accomplishment . These certificates are proof that you completed an online course on our platform and can easily be shared with its unique link.

Instructors & Guests
What You Need to Take This Course
Additional Information

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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