Mélytanulású

Mélytanulású is a Hungarian term that translates to "deep learning" in English. It refers to a subset of machine learning techniques that are based on artificial neural networks with multiple layers. These deep neural networks are designed to learn representations of data with multiple levels of abstraction.

The core idea behind mélytanulású is to process data through a series of layers, where each layer transforms the input data into a more abstract and refined representation. Early layers might detect simple features like edges or corners in an image, while deeper layers combine these features to recognize more complex patterns such as objects or faces. This hierarchical learning process allows mélytanulású models to automatically discover and learn intricate patterns from raw data without requiring extensive manual feature engineering.

Mélytanulású has achieved state-of-the-art results in various fields, including computer vision (image recognition, object detection), natural language processing (machine translation, text generation), speech recognition, and recommendation systems. Its success is largely attributed to the availability of large datasets and the significant advancements in computational power, particularly through the use of GPUs.

Key architectures within mélytanulású include Convolutional Neural Networks (CNNs), which are highly effective for image-related tasks, and Recurrent Neural Networks (RNNs), which are well-suited for sequential data like text or time series. More recently, Transformer models have gained prominence, revolutionizing natural language processing. The development and application of mélytanulású continue to drive innovation across many technological domains.