mohammedbehjoo/-artificial-intelligence-in-architecture-exploring-GANs

This workshop is going to be seven sessions of 3 hours. It consists of three major parts: first, Fundamentals of deep learning, which is about the history of artificial intelligence and the relationship between AI, machine learning, and deep learning. It is also about the mathematics of neural networks. After that, we are going to learn about the basics of neural networks and machine learning. Second, we are going more in-depth in computer vision; we train a neural network from scratch. Then we learn about “transfer learning,” which means how to use a pre-trained neural network. Finally, and most importantly, the last part is about “Generative adversarial networks” (GAN). We will have some experiments with “ neural style transfer” as an example. After that, we will learn about GANs, specifically CycleGAN, and how to implement them. After this workshop, you know the basics of artificial intelligence, the relationship between machine learning and deep learning. You understand how neural networks work; you can make and implement deep learning models to classify images. You can also make Generative Adversarial Networks to synthesize new images and broaden your horizons.

Jupyter Notebook