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Offered By: IBM

Creating anime characters using DCGANs and Keras

Mass production of millions of unique anime characters is nearly impossible for the best painter, but it is easy using machine learning method! In the guided project, you will have the chance to build machine learning models and produce the anime characters for yourself. After then, you will also handle the machine learning method called Deep Convolutional Generative adversarial networks (DCGANs), which is used for the mass anime production.

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Guided Project

Machine Learning

601 Enrolled
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At a Glance

Mass production of millions of unique anime characters is nearly impossible for the best painter, but it is easy using machine learning method! In the guided project, you will have the chance to build machine learning models and produce the anime characters for yourself. After then, you will also handle the machine learning method called Deep Convolutional Generative adversarial networks (DCGANs), which is used for the mass anime production.

About

You are hired by a video game company as a data scientist. The company is facing challenges and needs you to save their business.

The game is famous for its unique characters for every player. With the growth of the player amount, it comes to be a nearly impossible mission for the artist to hand plot the characters for millions of players. But your boss plans to keep the unique character-creating function in the game to keep the customers.

How can we mass-produce anime characters using a machine-learning method?
You will create anime characters like the ones below,
using the DCGANs model in this guided project.



As a data scientist, you know that Generative adversarial networks (GAN) can help with the task.
GANs is a class of machine learning frameworks, which could generate new and realistic photograph that is authentic to human observers. Moreover, applying the Convolutional networks (CNNs) to GANs models could facilitate the photo generating model. The combined method is called Deep Convolutional Generative Adversarial Networks (DCGANs). 

You are going to train a DCGANs model, using the existing character, for the further massive unique anime characters production for the video game.


A Look at the Project Ahead

In the guided project, you will first learn the basic about GANs, using toy data to understand what generator and discriminator is.
In the second half of the guided project, you will train Deep Convolutional Generative Adversarial Networks (DCGANs) models to create anime characters.
After the guided project, you will be able to:
  • know the basic GANs
  • implement GANs to datasets
  • understand how to train DCGANs 
  • produce a large amount of unique photos using DCGANs
  • understand how changing the input of the latent space of DCGANs changes the generated image 

What You'll Need

This course is good for intermediate learners in Machine Learning and Data Science. 
Knowing basic Python usage in data science is suggested before you start this guided project. 
We recommend using the IBM Skills Network Labs environment for this guided project. Everything you need to complete this project will be provided to you via the Skills Network Labs. The platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer or Safari.

Level

Intermediate

Industries

Information Technology

Skills You Will Learn

Data Science, Deep Learning, Generative AI, Keras, LLM, Python

Language

English

Course Code

GPXX0XCEEN

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