Mélytanulás

Mélytanulás, often translated as deep learning, is a subfield of machine learning that focuses on artificial neural networks with multiple layers. These networks are designed to learn representations of data with multiple levels of abstraction. The "deep" in deep learning refers to the depth of these layers, as opposed to shallow learning methods that use fewer layers.

The core idea behind deep learning is to allow a machine to learn from examples in much the same way as humans do. Instead of being explicitly programmed with rules, deep learning models are trained on vast amounts of data. During training, the network adjusts its internal parameters to identify patterns and features within the data. This allows it to perform tasks such as image recognition, speech recognition, natural language processing, and even generate creative content.

Deep learning algorithms have achieved state-of-the-art results in many areas. Their success is largely attributed to the availability of large datasets and the significant advancements in computing power, particularly the use of Graphics Processing Units (GPUs). While powerful, deep learning models can be computationally expensive to train and often require careful tuning of hyperparameters. They are also sometimes considered "black boxes" due to the difficulty in fully interpreting the decision-making process within their complex network structures.