Published 6/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 1.62 GB | Duration: 3h 48m
Build and train Generative Adversarial Networks in Python. Effectively apply pre-trained models for generative art!
What you'll learn
Become AI art artisan (generative artist)
Build your own GAN from scratch
Train GAN on a selected set of images
Generate beautiful AI art with pre-trained models
Create pieces of AI art with your own models
Understand how Neural Networks and Generative Adversarial Networks (GAN) work
Work in your AI workshop locally (CPU training) or remotely (GPU training)
Start and manage AI painting project
Requirements
Basic familiarity with python programming
Ability to work in a Docker container will be a bonus
Description
Did you know that computers can generate pictorial art? Have you ever wondered how can they do that? Did you know that the art generated by Artificial Intelligence (AI) has been sold on auctions for thousands of $$$?Welcome to Artem Ex Machina™! Here you will learn how to become Artificial Intelligence (AI) Artisan by building, training and applying Generative Artificial Networks (GANs) - deep learning networks behind AI generated art. What makes this course unique is the combination of three elements:Building, training and using GANs from scratchApplying pre-trained GANs for beautiful AI*ArtsStarting, maintaining and managing AI Arts projectsBy the end of this course you will, therefore, be able to:Build and train GANs using your own set of training imagesCreate pieces of AI Arts with publicly available, FREE, and amazing quality pre-trained GANs modelsUnderstand how Neural Networks, in general, and GANs, in particular, workEffectively manage AI Arts projects To get a taster on the kind of images you'll be able to generate by completing this course, have a look at the logo of this course, which has 6 examples of images generated with GANs
The course uses Python as a programming language upon which you'll be able to build, train and use your own GANs. Not only you'll gain practical capabilities when it comes to developing and diagnosing these deep learning models but also you'll gain a proper understanding of theory behind them. Since managing and, potentially, sharing your working environment behind GANs is an important part of the workflow of the generative artist, an integral part of the course is provision of AI-Art-compatible Docker containers and teaching you how to effectively use them. Understanding how GANs work combined with the ability to build them, will give you a strong generative artisanship acumen, and hence strong competitive advantage in the world of Data Science. In addition to that you'll learn how to make an effective use of pre-trained models that were trained for thousands of hours, to create high quality art. Therefore, you'll be able to work on your own local computer as well as remote high performance computing service such as Google Colab using Graphic Processing Units! The course is split into 6 sections culminating with a 3 capstone projects that you can add to your portfolio:Introduction. Here you'll learn how to setup your local working environment PLUS how to train and diagnose a hand-written digits GANWhat is a Generative Adversarial Network? Here you'll learn how GANs work and how they are trained PLUS how to train and apply GAN of images of clothing AI Arts projects. Here you'll learn how to work locally vs how to work remotely and principles of AI Arts project management PLUS how to efficiently download bulk of images for your GAN trainingGAN architectures. Here you'll learn how to build and train GAN models from scratch to produce complex grayscale and colour images GAN+CLIP. Here you'll learn how to use pre-trained models for beautiful AI Arts generationCapstone projects. Here you'll have the chance to complete three capstone projects to consolidate what you've learnt in the course. Start you Artem Ex Machina™ journey now!Overview
Section 1: Introduction
Lecture 1 What will you get from the course?
Lecture 2 Intro
Lecture 3 Begin your first GAN training!
Lecture 4 Load a Docker container
Lecture 5 Compile a Docker container
Lecture 6 Connect to a Docker container
Lecture 7 Run a simple GAN training procedure
Lecture 8 Examine the evolution of GAN
Lecture 9 Switch the container on and off
Lecture 10 Change the memory and CPU of your container
Lecture 11 Train your first GAN challenge
Lecture 12 Train your first GAN solution
Section 2: What is a Generative Adversarial Network?
Lecture 13 Intro
Lecture 14 What is Deep Learning?
Lecture 15 Artificial Neural Networks and their anatomy
Lecture 16 Training an Artificial Neural Network
Lecture 17 What is a Generative Adversarial Network?
Lecture 18 Mnist clothing GAN challenge
Lecture 19 Mnist clothing GAN solution
Lecture 20 Create pieces of AI Arts with your own models
Section 3: AI Arts projects
Lecture 21 Intro
Lecture 22 Local CPU vs remote GPU
Lecture 23 Introduction to Google Colab
Lecture 24 Principles of AI arts project management
Lecture 25 Easily collect your custom training images
Section 4: GAN architectures
Lecture 26 Intro
Lecture 27 Unpacking basic GAN architecture
Lecture 28 From scratch handwritten digit GAN
Lecture 29 From scratch handwritten digit GAN: solution
Lecture 30 CNNs & complex GAN architecture
Lecture 31 Grayscale landscape GAN
Lecture 32 Grayscale landscape GAN: solution
Lecture 33 Colour paintings GAN
Lecture 34 Colour painting GAN: solution
Section 5: GAN+CLIP
Lecture 35 Intro
Lecture 36 Introduction to GAN+CLIP
Section 6: Capstone projects
Lecture 37 Intro
Lecture 38 Capstone 1
Lecture 39 Capstone 2
Lecture 40 Capstone 3
Lecture 41 Course summary
Data scientists and analyst that want to be able to build and apply Generative Adversarial Network models,Data scientists and analyst that want to learn or enhance their Deep Learning skills,STEM professionals wanting to expand into emerging word of AI arts,Anyone who wants to learn how to start AI Arts workshop,Anyone interested in AI arts and that wants to understand it
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