
What you would learn in Artem Ex Machina™: become AI Arts Artisan from the ground up course?
Have you ever heard that computers could produce art with pictures? Did you wonder how they accomplished this? Have you heard that art created from Artificial Intelligence (AI) has been auctioned off for thousands of dollars? $$$?
Hello and welcome to Artem Ex Machina(TM)! Learn how to become an Artificial Intelligence (AI) Artisan through creating, training, and applying generative Artificial Networks (GANs), which are deep learning networks that are behind AI-created art. The thing that makes this course special lies in the combination of 3 aspects:
Training, building and employing GANs from the ground up
Utilizing GANs that have been trained for stunning AI Arts
Establishing, maintaining, as well as overseeing AI Arts projects
At the end of this course, you will consequently be able:
Create and train GANs with your set of training images
Create works from AI Arts with publicly available, FREE, and stunning high-quality GANs models that are already trained
Know the way Neural Networks, in general, and GANs precisely function.
Manage AI Arts projects effectively
For a glimpse of the kinds of images you'll be able to create after taking this course, look at this logo for this course. It contains six images that were created using GANs :)
The course employs Python as a programming language, upon which you'll be able to develop, train and implement the models you create yourself. GANs. You'll not only gain the practical skills related to developing and analyzing deep learning models, but you'll also acquire an understanding of the theories of the models. Because managing and possibly sharing your workspace used to develop GANs is a crucial aspect of the workflow for the creator, an integral component of the course is the provision of AI-Art compatible Docker containers and teaching how to utilize them. Knowing how GANs operate, when combined with the capability of building them, will provide you with the ability to build a solid generative artisanship skill and, consequently, an advantage over your competitors in the realm of Data Science. Additionally, you'll be taught how to make the best use of pre-trained models, which have been taught for thousands of hours, and to produce high-quality artwork. Thus, you'll be able to operate on your local computer and remote high-performance computing services like Google Colab by using graphic Processing Units!
The course is broken down into six sections and concludes with 3 capstone projects you can include in your portfolio.
Introduction. Learn how to set up your working environment local to you, and learn how you can train your employees and detect digits written by hand.
What is a Generative Adversarial Network? This article will explain how GANs operate and how they're trained, and the methods to teach and implement GAN of clothing images.
AI Arts projects. Learn the basics of working locally vs. remote work and the fundamentals that govern AI Arts project management PLUS how to download large quantities of images for GAN training effectively
GAN architectures. This article will teach you how to create and develop GAN designs from scratch, to produce intricate color and grayscale images.
GAN+CLIP. This is where you'll discover how to use models trained to produce a gorgeous AI Arts generation.
Capstone project. This is where you'll get the chance for three final capstone tasks to help you consolidate the knowledge you've acquired throughout the course.
Course Content:
- Become an AI art artisan (generative artist)
- Make an individual GAN
- Train GAN on an appointed collection of images
- Create beautiful AI art using pre-trained models
- Create works of AI art using your models
- Learn the way Neural Networks and Generative Adversarial Networks (GAN) are used
- Participate in your AI workshop in your local area (CPU learning) as well as online (GPU training)
- Begin with and oversee the AI painting project
Download Artem Ex Machina™: become AI Arts Artisan from the ground up from below links NOW!
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