TensorFlow: Advanced Techniques Specialization

The DeepLearning.AI TensorFlow Developer Professional Certificate equips you with the foundational knowledge to create entry-level TensorFlow models using the Sequential API and prepares you for the Google TensorFlow Developer Certificate exam. 

In this new TensorFlow Specialization, you will expand your skill set and take your understanding of TensorFlow techniques to the next level. You will learn how to use the Functional API for custom training, custom layers, and custom models. You will be equipped to master TensorFlow in order to build powerful applications for complex scenarios.

Specialization: Gain practical knowledge of and hands-on training in advanced TensorFlow techniques such as style transfer (paint one picture in the style of another), object detection (detect where an object is in a picture), and generative machine learning (generating new images from scratch).

Course 1: Understand the underlying basis of the Functional API and build exotic non-sequential model types, custom loss functions, and layers. 

Course 2: Learn how optimization works and how to use GradientTape and Autograph. Optimize training in different environments with multiple processors and chip types.

Course 3: Practice object detection, image segmentation, and visual interpretation of convolutions.

Course 4: Explore generative deep learning and how AIs can create new content, from Style Transfer through Auto Encoding and VAEs to Generative Adversarial Networks.

The TensorFlow: Advanced Techniques Specialization provides an accessible pathway for early and mid-career software and machine learning engineers with a foundational knowledge of TensorFlow looking to master TensorFlow by teaching them how to use advanced features in order to expand their skill set and build powerful models.

Learners should have a working knowledge of AI and deep learning. They should have intermediate Python skills (understanding of decorators and context managers is preferred) as well as some experience with any deep learning framework (TensorFlow, Keras, or PyTorch). Learners should be proficient in basic calculus, linear algebra, and statistics.

We highly recommend that you complete the Deep Learning Specialization prior to starting this Specialization.

After completing this Specialization, you will gain practical knowledge and hands-on training in advanced TensorFlow techniques such as style transfer, object detection, and generative machine learning.

This Specialization was created by Laurence Moroney. 

Laurence leads AI Advocacy at Google, with a vision to make AI easy for developers and to widen access to ML careers for everyone. He’s written dozens of programming books, the most recent being ‘AI and ML for Coders’ at O’Reilly. Laurence believes that MOOCs are one of the greatest ways to learn, and is excited to create TensorFlow Specializations with DeepLearning.AI on Coursera. 

When not working with technology, he’s an active member of the Science Fiction Writers of America and has authored several sci-fi novels and comics books and a produced screenplay. Laurence is based in Washington state, where he drinks way too much coffee.

This is a Specialization made up of 4 courses.

We recommend taking the courses in the prescribed order for a logical and thorough learning experience.

This Specialization consists of four courses. At the rate of 5 hours a week, it typically takes 3-4  weeks to complete each course.

No

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.