More Information
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
About

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.

Features
  • Understand the buzz surrounding Generative Adversarial Networks and how they work, in the simplest manner possible.
  • Develop generative models for a variety of real-world use-cases and deploy them to production
  • Contains intuitive examples and real-world cases to put the theoretical concepts explained in this course to practical use
Course Length 1 hour 36 minutes
ISBN 9781788990899
Date Of Publication 23 Jan 2018

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.