Deep Learning — Neural Networks, CNN, RNN, Transformers, and How They Learn

This hub is a navigation map designed to help you move through the core flow of where deep learning starts → why it diverged from earlier approaches → what depth really means → how training paradigms changed → how it expanded into modern AI, then jump straight to the next article you need.
It first gives you one main entry point and recommended routes by learning goal, so you can decide immediately where to click next.

04/17/2026

End-to-End Model — An AI System Architecture That Maps Inputs Directly to Outputs

An End-to-End Model is an AI system architecture that transforms input data directly into the final output within a single model, instead of dividing the task into multiple human-designed intermediate modules. Its core idea is not merely how the model is trained, but how the model’s input-output architecture is designed. In other words, an End-to-End Model takes raw inputs such as images·speech·or text and directly produces final predictions, generated sentences, control values, or classification results without explicitly separating intermediate outputs.

10/29/2025