1. Introduction

Goal: This tutorial aims to introduce the use of Artificial Intelligence (AI) in the field of graphic design. It will provide a practical guide on how AI can be used to create and manipulate visual content, such as logos and images.

What you'll learn: By the end of this tutorial, you will have a basic understanding of AI in graphic design, the ability to implement AI tools in creating visual content, and an idea of how AI can benefit and revolutionize the design process.

Prerequisites: Basic understanding of graphic design principles and familiarity with Python programming language is recommended.

2. Step-by-Step Guide

Concepts:

• AI in Graphic Design: AI has the potential to automate and simplify the design process, creating designs based on data inputs and programmed algorithms.

• Generative Adversarial Networks (GANs): GANs are a class of AI algorithms used in unsupervised machine learning, implementing two neural networks contesting with each other in a game.

Examples:

Let's take an example of using GANs to generate images.

# Import necessary libraries
from keras.datasets import mnist
from keras.layers import Input, Dense, Reshape, Flatten
from keras.layers import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.models import Sequential, Model
from keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np

Best Practice and Tips:

• Start with a clear objective: Understand what you want your AI to create.
• Test and iterate: AI in graphic design requires lots of trial and error.

3. Code Examples

# This code is an example of using Python and Keras library to create a simple GAN.

def build_generator():
    model = Sequential()

    model.add(Dense(256, input_dim=100))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(512))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(1024))
    model.add(LeakyReLU(alpha=0.2))
    model.add(BatchNormalization(momentum=0.8))
    model.add(Dense(np.prod((28, 28, 1)), activation='tanh'))
    model.add(Reshape((28, 28, 1)))

    noise = Input(shape=(100,))
    img = model(noise)

    return Model(noise, img)

4. Summary

This tutorial introduced AI in graphic design and discussed the use of GANs to create images. The power of AI can greatly enhance the design process, offering automated, efficient, and creative solutions.

5. Practice Exercises

1. Try to create a GAN that can generate simple shapes like circles, squares, and triangles.

2. Modify the GAN code to generate a different type of images, like faces or animals.

3. Experiment with different parameters in the GAN model to see how they affect the output.

Remember, practice and experimentation are key in understanding and leveraging the power of AI in graphic design.