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MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
This Specialization is for early and mid-career software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models.
Course 4 of 4 in the TensorFlow: Advanced Techniques Specialization.
Syllabus
WEEK 1
Style Transfer
This week, you will learn how to extract the content of an image (such as a swan), and the style of a painting (such as cubist, or impressionist), and combine the content and style into a new image. This is called neural style transfer, and you'll learn how to extract these kinds of features using transfer learning.
WEEK 2
AutoEncoders
This week, you’ll get an overview of AutoEncoders and how to build them with TensorFlow. You'll learn how to build a simple AutoEncoder on the familiar MNIST dataset, before diving into more complicated deep and convolutional architectures that you'll build on the Fashion MNIST dataset. You'll get to see the difference in results of the DNN and CNN AutoEncoder models, and then identify ways to denoise noisy images. You'll finish the week building a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one!
WEEK 3
Variational AutoEncoders
This week you will explore Variational AutoEncoders (VAEs) to generate entirely new data. In this week’s assignment, you will generate anime faces and compare them against reference images.
WEEK 4
GANs
This week, you’ll learn about GANs. You'll learn what they are, who invented them, their architecture and how they vary from VAEs. You'll get to see the function of the generator and the discriminator within the model, and the concept of 2 training phases and the role of introduced noise. Then you'll end the week building your own GAN that can generate faces! How cool is that!
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.