Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, Ch

Applied Generative AI for Beginners: Practical Knowledge on Diffusion Models, Ch

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and GANs

In this post, we will explore the basics of Applied Generative AI for beginners, focusing on diffusion models and GANs (Generative Adversarial Networks). Generative AI has become increasingly popular in recent years, with applications in image generation, text generation, and more.

Diffusion models are a type of generative model that models the conditional distribution of the next data point given all previous data points. This allows for the generation of high-quality samples that capture the underlying distribution of the data. Diffusion models have been used in various applications, including image generation and video prediction.

On the other hand, GANs are a type of generative model that consists of two neural networks – a generator and a discriminator. The generator generates samples, while the discriminator tries to differentiate between real and generated samples. Through this adversarial training process, GANs can produce realistic samples that fool the discriminator.

In this post, we will cover the basics of both diffusion models and GANs, including how they work, their applications, and how to get started with implementing them. Stay tuned for more practical knowledge on Applied Generative AI for beginners!
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