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March 4, 2024
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This course is part of TensorFlow: Advanced Techniques Specialization
Instructors: Laurence Moroney
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22,790 already enrolled
(296 reviews)
Recommended experience
Intermediate level
Python, TensorFlow, and deep learning
TensorFlow's Functional API and Gradient Tape (covered in course 1 and 2 of this specialization)
(296 reviews)
Recommended experience
Intermediate level
Python, TensorFlow, and deep learning
TensorFlow's Functional API and Gradient Tape (covered in course 1 and 2 of this specialization)
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In this course, you will:
a) Learn neural style transfer using transfer learning: extract the content of an image (eg. swan), and the style of a painting (eg. cubist or impressionist), and combine the content and style into a new image. b) Build simple AutoEncoders on the familiar MNIST dataset, and more complex deep and convolutional architectures on the Fashion MNIST dataset, understand the difference in results of the DNN and CNN AutoEncoder models, identify ways to de-noise noisy images, and build a CNN AutoEncoder using TensorFlow to output a clean image from a noisy one. c) Explore Variational AutoEncoders (VAEs) to generate entirely new data, and generate anime faces to compare them against reference images. d) Learn about GANs; their invention, properties, architecture, and how they vary from VAEs, understand the function of the generator and the discriminator within the model, the concept of 2 training phases and the role of introduced noise, and build your own GAN that can generate faces. The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. 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.
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.
13 videos8 readings1 assignment1 programming assignment3 ungraded labs
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!
6 videos1 reading1 assignment1 programming assignment5 ungraded labs
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.
6 videos3 readings1 assignment1 programming assignment1 ungraded lab
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!
7 videos10 readings1 assignment1 programming assignment3 ungraded labs
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
Instructor ratings
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
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Reviewed on Jun 19, 2024
Excellent course. The only reason I don't opt to 5-rate it is because, coming from completing courses by Andrew Ng, I kind of wanted a more mathematics/theory- driven course.
Reviewed on Apr 18, 2021
Excellent course. Highly recommended. Please make a separate course on GAN. Use TensorFlow instead of PyTorch
Reviewed on Mar 21, 2024
Although the VAE module was a bit difficult, I found this course helpful to refine my deep learning knowledge.
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