Tutorials on Genai

Explore top techniques for fine-tuning LLMs with LoRA. Enhance AI inferences and applications by leveraging the latest in prompt engineering.

GANs, or Generative Adversarial Networks, involve two primary components: the generator and the discriminator. These neural networks operate under adversarial principles, each with a distinct function. While the generator's role is to create data that resembles actual data, the discriminator's task is to evaluate this data's authenticity. The two networks do not function in isolation. They are trained concurrently. This simultaneous training ensures the networks refine their abilities in response to each other's improvements. Each iteration enables the generator to produce output closer to real data, challenging the discriminator's capacity to distinguish between real and synthetic data. The adversarial relationship acts as a feedback loop. The generator seeks to outsmart the discriminator, pushing it toward higher realism. Conversely, the discriminator continuously adapts to better identify discrepancies in the generator's output. The iterative process results in progressively more authentic data generation.