#deep-learning

Automatischer Uebersetzungsentwurf (German) for "Transformer": A neural network architecture introduced in 2017 ("Attention Is All You Need") that underlies virtually all modern language models. Transformers use self-attention mechanisms to process entire sequences in parallel, capturing long-range dependencies that earlier recurrent architectures struggled with.

“Beispielentwurf: Every major LLM from GPT to Claude is built on the transformer architecture.”

Automatischer Uebersetzungsentwurf (German) for "Diffusion Model": A class of generative AI model that learns to create images, audio, or video by reversing a noise-adding process. During training the model learns to denoise progressively; during generation it starts from pure noise and iteratively refines it into a coherent output. Stable Diffusion and DALL·E 3 are prominent examples.

“Beispielentwurf: The diffusion model generated photorealistic product photos from text descriptions in seconds.”

Automatischer Uebersetzungsentwurf (German) for "Neural Network": A computational model loosely inspired by biological neurons, consisting of interconnected layers of mathematical functions (nodes) that transform input data into output predictions. Neural networks learn by adjusting the weights of connections through exposure to training data.

“Beispielentwurf: The neural network learned to recognize handwritten digits with over 99% accuracy.”