Generative Deep Learning with TensorFlow

Go to Course: https://www.coursera.org/learn/generative-deep-learning-with-tensorflow

Introduction

### Course Review: Generative Deep Learning with TensorFlow **Overview:** If you’re excited about the intersection of creativity and technology, look no further than the "Generative Deep Learning with TensorFlow" course on Coursera. This course provides a hands-on journey through the cutting-edge techniques in generative deep learning, equipping you with the skills to create stunning visuals and unique data outputs using neural networks. By the end of the course, you will have a deep understanding of four vital concepts in generative deep learning: Neural Style Transfer, AutoEncoders, Variational AutoEncoders (VAEs), and Generative Adversarial Networks (GANs). Each week builds on the principles learned in the previous ones, providing a structured yet dynamic learning experience. --- **Course Breakdown:** **Week 1: Style Transfer** In the opening week, the course introduces you to neural style transfer - a fascinating technique that allows you to merge the content of one image with the style of another, such as combining a swan with the keen brushstrokes of a cubist painting. Utilizing transfer learning, you’ll learn how to extract and integrate these visual elements, making this week both informative and creatively engaging. **Week 2: AutoEncoders** The second week dives into AutoEncoders, starting with the foundational MNIST dataset before progressing to the more complex Fashion MNIST dataset. You'll gain hands-on experience building simple AutoEncoders and observing the differences in result quality between Deep Neural Network (DNN) and Convolutional Neural Network (CNN) AutoEncoders. Additionally, you will learn practical techniques for image denoising, culminating in the construction of a CNN AutoEncoder that can transform a noisy image into a clean representation. **Week 3: Variational AutoEncoders (VAEs)** In week three, you will expand your generative skills by exploring VAEs, which allow for the generation of entirely new data. Notably, you will engage in an assignment focused on generating anime faces and evaluating them against reference images. This week fosters creativity and introduces students to innovative applications of autoencoders. **Week 4: GANs** The final week focuses on Generative Adversarial Networks (GANs), delving into their architecture and operational mechanics. You will learn about the generator-discriminator relationship and how the two networks compete to generate realistic outputs. By the end of the week, you’ll create your own GAN capable of generating faces, showcasing the culmination of your skills throughout the course. --- **Recommendation:** I wholeheartedly recommend "Generative Deep Learning with TensorFlow" for anyone interested in deep learning, whether you’re a beginner or have some prior experience. The course is designed to be accessible yet thorough, making it suitable for individuals from various backgrounds. Each week's content is engaging, and the combination of theory and practical applications ensures a comprehensive learning experience. Moreover, the ability to create art through neural style transfer and generate faces with GANs is not only exciting but also invaluable for those looking to work in fields like artificial intelligence, computer vision, or creative technology. By the end of the course, you’ll not only have built impressive models but also developed a nuanced understanding of generative techniques that are shaping the future of machine learning. Enrolling in this course will prepare you for both academic pursuits in AI and practical applications, making it a worthwhile investment in your learning journey.

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.

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!

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.

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!