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You're reading from  Mastering PyTorch

Product typeBook
Published inFeb 2021
Reading LevelIntermediate
PublisherPackt
ISBN-139781789614381
Edition1st Edition
Languages
Python
Tools
PyTorch
Concepts
Deep Learning
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Author (1)
Ashish Ranjan Jha
Ashish Ranjan Jha
author image
Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.
Read more about Ashish Ranjan Jha

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Summary

GANs have been an active area of research and development in recent years, ever since their inception in 2014. This chapter was an exploration of the concepts behind GANs, including the components of GANs, namely, the generator and the discriminator. We discussed the architectures of each of these components and the overall schematic of a GAN model.

Next, we did a deep dive into a particular type of GAN – the DCGAN. With the help of an exercise, we built a DCGAN model from scratch using PyTorch. We used the MNIST dataset to train the model. The generator of the trained DCGAN model successfully generated realistic-looking fake images of handwritten digits after 10 epochs of training.

In the last section of this chapter, we explored another type of GAN, which is used for the task of image-to-image translation – the pix2pix model. Instead of working on just a pair of images, the pix2pix GAN model is architectured to generalize any image-to-image translation...

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Mastering PyTorch
Published in: Feb 2021Publisher: PacktISBN-13: 9781789614381
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Author (1)

author image
Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.
Read more about Ashish Ranjan Jha

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