"Gradient-Based Learning Applied to Document Recognition"

"Gradient-Based Learning Applied to Document Recognition" is a process where computers learn to identify text and patterns in documents by adjusting their internal settings. It uses a method called gradient descent, which involves calculating how small changes affect the system's output and then updating the model to improve accuracy. Essentially, the computer iteratively fine-tunes itself to recognize characters, words, and formats more reliably. This approach enables machines to efficiently learn from examples, making automated reading and understanding of documents more accurate and adaptable over time.