Learning Generative Adversarial Networks [Video]

Preview in Mapt

Code Files

Learning Generative Adversarial Networks [Video]

Kuntal Ganguly
Tuesday, January 23, 2018

Build image generation and semi-supervised models using Generative Adversarial Networks

Quick links: > What will you learn?> Table of content

Mapt Subscription

FREE

£24.99/m after trial

Video

£94.86

RRP £111.58

Save 14%

What do I get with a Mapt Pro subscription?

Unlimited access to all Packt’s 5,000+ eBooks and Videos
Early Access content, Progress Tracking, and Assessments
1 Free eBook or Video to download and keep every month after trial

What do I get with an eBook?

Download this book in EPUB, PDF, MOBI formats
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

What do I get with Print & eBook?

Get a paperback copy of the book delivered to you
Download this book in EPUB, PDF, MOBI formats
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

What do I get with a Video?

Download this Video course in MP4 format
DRM FREE - read and interact with your content when you want, where you want, and how you want
Access this title in the Mapt reader

£0.00

£94.86

£25.00 p/m after trial

RRP £111.58

Subscription

Video

Start 14 Day Trial

Frequently bought together

Learning Generative Adversarial Networks [Video]

£ 111.58

£ 94.86

Learning Generative Adversarial Networks [Video]

Jan 2018

1 hour and 36 minutes

£ 94.86

Neural Networks in Machine Learning for Developers [Video]

£ 111.58

£ 94.86

Neural Networks in Machine Learning for Developers [Video]

Jan 2018

44 minutes

£ 94.86

Buy 2 for £31.68
Save £159.58

Add to Cart

Video Details

ISBN 139781788990899

Course Length1 hour and 36 minutes

Video Description

Generative models are gaining a lot of popularity among data scientists, mainly because they facilitate the building of AI systems that consume raw data from a source and automatically build an understanding of it.

Unlike supervised learning methods, generative models do not require labeling data, which makes for an interesting system to use. This video will help you build and analyze deep learning models and apply them to real-world problems. It will help readers develop intelligent and creative application from a wide variety of datasets, mainly focusing on visuals or images.

The video begins with the basics of generative models, as you get to know the theory behind Generative Adversarial Networks and its building blocks. In this video, you'll see how to overcome the problem of text-to-image synthesis with GANs, using libraries such as Tensorflow, Keras, and PyTorch.

Transferring styles from one domain to another becomes a headache when working with huge data sets. Using real-world examples, we will show how you can overcome this. You will understand and train Generative Adversarial Networks, use them in a production environment, and implement tips to use them effectively and accurately.

Style and Approach

This course adopts a problem/solution approach. Each video focuses on a particular task at hand, and is explained in a very simple, easy-to-understand manner.

Unsupervised Deep Learning with GAN

The Course Overview

Introduction to Deep Learning

Automating Human Tasks with Deep Neural Networks

Implementation of GAN

Challenges of GAN Models

Improved Training Approaches and Tips for GAN

Transfer Image Style Across Various Domains

Introduction to Conditional GAN

Training Procedure of BEGAN

Image to Image Style Transfer with CycleGAN

Building Realistic Images from Text

Introduction to StackGAN

Discovering Cross-Domain Relationship with DiscoGAN

Generating Handbags from Edges with PyTorch

Gender Transformation Using PyTorch

Taking Machine Learning to Production

Building an Image Correction System Using DCGAN

Microservice Architecture Using Containers

Various Approaches to Deploy Deep Models

Serving Keras-Based Deep Models on Docker

Serverless Image Recognition

What You Will Learn

Understand the unsupervised deep learning concept
Transfer images to image styles across various domains
Build a realistic image from text
Deploy deep models on the cloud with GKE

Authors

Kuntal Ganguly

Kuntal Ganguly is a big-data analytics engineer focused on building large-scale, data-driven systems using big data frameworks and machine learning. He has around 7 years' experience building big-data and machine learning applications.

Kuntal provides solutions to cloud customers involving building real-time analytics systems using managed cloud services and open source Hadoop ecosystem technologies such as Spark, Kafka, Storm, Solr, and so on, along with machine learning and deep learning frameworks.

Kuntal enjoys hands-on software development and has single-handedly conceived, architected, developed, and deployed several large-scale distributed applications.
He is a machine learning and deep learning practitioner and is very passionate about building intelligent applications.

His LinkedIn profile is as follows: https://in.linkedin.com/in/kuntal-ganguly-59564088.

Unsupervised Deep Learning with GAN

The Course Overview

Introduction to Deep Learning

Automating Human Tasks with Deep Neural Networks

Implementation of GAN

Challenges of GAN Models

Improved Training Approaches and Tips for GAN

Transfer Image Style Across Various Domains

Introduction to Conditional GAN

Training Procedure of BEGAN

Image to Image Style Transfer with CycleGAN

Building Realistic Images from Text