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.Continue reading
Machine Learning325 Enrolled
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.
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 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
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.
Skills You Will Learn
Python, Data Science, Deep Learning, Keras, Generative AI, LLM
November 07, 2022
Senior Data Scientist at IBM
Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.Read more
Data Scientist at IBM
I am an aspiring Data Scientist at IBM with extensive theoretical/academic, research, and work experience in different areas of Machine Learning, including Classification, Clustering, Computer Vision, NLP, and Generative AI. I've exploited Machine Learning to build data products for the P&C insurance industry in the past. I also recently became an instructor of the Unsupervised Machine Learning course by IBM on Coursera!Read more
Data scientist at IBM
Data science is easy and helpful! I want to let everyone know data science and help everyone using it for everyday life! Not only being a Data science guide person but also making friends, I want to make friends with peoples like you! As a data scienist, I hope my spread data science could help my friend!Read more