Generate Synthetic Images with DCGANs in Keras
247 ratings

14,656 already enrolled
Understand Deep Convolutional Generative Adversarial Networks (DCGANs and GANs)
Design and train DCGANs using the Keras API in Python
247 ratings
14,656 already enrolled
Understand Deep Convolutional Generative Adversarial Networks (DCGANs and GANs)
Design and train DCGANs using the Keras API in Python
In this hands-on project, you will learn about Generative Adversarial Networks (GANs) and you will build and train a Deep Convolutional GAN (DCGAN) with Keras to generate images of fashionable clothes. We will be using the Keras Sequential API with Tensorflow 2 as the backend. In our GAN setup, we want to be able to sample from a complex, high-dimensional training distribution of the Fashion MNIST images. However, there is no direct way to sample from this distribution. The solution is to sample from a simpler distribution, such as Gaussian noise. We want the model to use the power of neural networks to learn a transformation from the simple distribution directly to the training distribution that we care about. The GAN consists of two adversarial players: a discriminator and a generator. We’re going to train the two players jointly in a minimax game theoretic formulation. This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed. Notes: - You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want. - This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Deep Learning
Machine Learning
Tensorflow
Computer Vision
keras
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Project Overview and Import Libraries
Load and Preprocess the Data
Create Batches of Training Data
Build the Generator Network for DCGAN
Build the Discriminator Network for DCGAN
Compile the Deep Convolutional Generative Adversarial Network (DCGAN)
Define the Training Procedure
Train DCGAN
Generate Synthetic Images with DCGAN
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
by EZ
Jun 8, 2020Everything was well explained and a very good project to get a good knowledge about GAN networks and its applications. Looking for more such projects.
by SS
Aug 12, 2020This was very good guided project to understand practically
by ST
Jul 20, 2020Excellent instructor. Dense with content and comments explaining bits of code.
by AG
Jun 13, 2020In this course, you will learn about a lot of different ways to join ideas to make more complex and interesting knowledge of keras
By purchasing a Guided Project, you'll get everything you need to complete the Guided Project including access to a cloud desktop workspace through your web browser that contains the files and software you need to get started, plus step-by-step video instruction from a subject matter expert.
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At the top of the page, you can press on the experience level for this Guided Project to view any knowledge prerequisites. For every level of Guided Project, your instructor will walk you through step-by-step.
Yes, everything you need to complete your Guided Project will be available in a cloud desktop that is available in your browser.
You'll learn by doing through completing tasks in a split-screen environment directly in your browser. On the left side of the screen, you'll complete the task in your workspace. On the right side of the screen, you'll watch an instructor walk you through the project, step-by-step.
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