Generative Adversarial Networks (GANs) and Relation Attention Networks (RANs) Introduction for beginners in AI/ML Model Creation
I've recently dived into the realm of AI/ML with my recent project of building a chatbot for my group project at college, and decided to start building a personal project as well. But while researching the available options, I realized that what I need isnt truly present as is in any LLM or LVM. So I had to dig deeper, towards the core of these models and found out about RANs and GANs. Let me break the concept down for you. GAN and RAN are both types of neural networks used for machine learning tasks, but they differ in their architectures and purposes: Generative Adversarial Networks (GANs): Purpose : Generate new data (images, text, music) that closely resembles existing data. Architecture : Two competing networks: 1. Generator: Creates new data by drawing from random noise. 2. Discriminator: Tries to distinguish real data from generated data. The generator learns to improve its creations by fooling the discriminator, leading to progressively more realistic and creative outputs....