GANs: Generative Adversarial Networks (GANs) are an innovative approach in machine learning where two neural networks, the generator and the discriminator, engage in a competitive game. The generator attempts to produce realistic data (such as images or text) from random noise, while the discriminator aims to distinguish between real and fake data. Through this adversarial process, the generator improves its output quality, generating data that becomes indistinguishable from real samples. GANs have revolutionized fields like image synthesis, art creation, and data augmentation, allowing for creative and realistic data generation.
Neural Networks: Neural networks are a class of machine learning models inspired by the structure of the human brain's neural connections. Composed of interconnected nodes, or neurons, organized in layers, neural networks are designed to learn complex patterns and representations from data. By adjusting weights and biases iteratively through backpropagation, neural networks can approximate complex functions and make predictions in various domains. Deep learning, a subset of neural networks with multiple hidden layers, has propelled advancements in computer vision, natural language understanding, and reinforcement learning, making significant contributions to modern AI technologies.
