Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Generative Models

In recent years, the field of artificial intelligence (AI) and machine learning (ML) has undergone a transformative shift, with generative models emerging as one of the most significant and impactful areas of research. Generative models, in essence, are models that can generate new data instances that resemble a given set of training data. Unlike discriminative models, which focus on classification tasks, generative models aim to understand and replicate the underlying structure of data, making them capable of generating images, text, audio, and even 3D objects. This introduction serves as a foundation for understanding the core principles behind the most important generative models: Autoencoders (AE), Variational Autoencoders (VAE), Masked Autoencoders (MAE), Generative Adversarial Networks (GANs), Diffusion Models, and models like GPT (Generative Pre-trained Transformers)