Image Super-Resolution GANs

Image Super-Resolution GANs, available at $49.99, has an average rating of 4, with 24 lectures, based on 28 reviews, and has 341 subscribers.

You will learn about Create a generator architecture that upsamples an image by 4 times in each dimension Create a discriminator architecture that scores both realism and fidelity to the original image Modify custom written Keras layers to accept input images of any size without rebuilding the model Train the models on a Cloud TPU through Google CoLab Use the trained generator in a practical application to upsample your own images This course is ideal for individuals who are Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity It is particularly useful for Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity.

Enroll now: Image Super-Resolution GANs

Summary

Title: Image Super-Resolution GANs

Price: $49.99

Average Rating: 4

Number of Lectures: 24

Number of Published Lectures: 24

Number of Curriculum Items: 24

Number of Published Curriculum Objects: 24

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Create a generator architecture that upsamples an image by 4 times in each dimension
Create a discriminator architecture that scores both realism and fidelity to the original image
Modify custom written Keras layers to accept input images of any size without rebuilding the model
Train the models on a Cloud TPU through Google CoLab
Use the trained generator in a practical application to upsample your own images

Who Should Attend

Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity

Target Audiences

Python + TensorFlow 2.0 developers who want to enlarge images with photorealistic detail and clarity

We’ve all seen the gimmick in crime TV shows where the investigators manage to take a tiny patch of an image and magnify it with unrealistic clarity. Well today, Generative Adversarial Networks are making the impossible possible.

Dive into this course where I’ll show you how easily we can take the fundamentals from my High Resolution Generative Adversarial Networks course and build on this to accomplish this impressive feat known as Super-resolution. Not only will you be able to train a Generator to magnify an image to 4 times it’s original size (that’s 16 times the number of pixel!), but it will take relatively little effort on our end.

Just as in the first course, we’ll use Python and TensorFlow 2.0 along with Keras to build and train our convolutional neural networks. And since training our networks will require a ton of computational power, we’ll once again use Google CoLab to connect to a free Cloud TPU. This will allow us to complete the training in just a few days without spending anything on hardware!

If this sounds enticing, take a few minutes to watch the free preview of the “Results!” lesson. I have no doubt that you will come away impressed.

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Prerequisites

Lecture 3: Getting the Most Out of This Course

Lecture 4: What is Super-resolution?

Chapter 2: Model Architecture

Lecture 1: Generator – High Level

Lecture 2: Generator – Handling a Variety of Image Sizes

Lecture 3: Generator – Details

Lecture 4: Discriminator – High Level

Lecture 5: Discriminator – Details

Lecture 6: Generator – Code

Lecture 7: Handling Various Image Sizes – Code

Lecture 8: Discriminator – Code

Chapter 3: Training

Lecture 1: Loss Function

Lecture 2: Training Overview

Lecture 3: Training Loop Code

Lecture 4: Training Loop Code – Resizing and Cropping

Lecture 5: Training Setup Code

Lecture 6: Visualizer Code

Lecture 7: Main Training Script and Colab Notebook

Lecture 8: Visualizations

Chapter 4: Super-resolution in Action!

Lecture 1: Main Upsampling Script

Lecture 2: Results!

Lecture 3: Limitation, Tips, and Tricks

Lecture 4: Conclusion

Instructors

Brad Klingensmith
Machine Learning Instructor

Rating Distribution

1 stars: 3 votes
2 stars: 0 votes
3 stars: 3 votes
4 stars: 6 votes
5 stars: 16 votes

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