mélytanulást

Mélytanulás, often translated as deep learning, is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the human brain, specifically the neural networks. These artificial neural networks consist of multiple layers, hence the term "deep." Each layer processes information and transforms it into a more abstract representation, allowing the model to learn complex patterns and features from data.

The power of deep learning lies in its ability to automatically learn hierarchical representations from raw data. For instance, in image recognition, the initial layers might detect simple edges and textures, while deeper layers combine these to recognize more complex shapes, objects, and eventually entire scenes. This automatic feature extraction eliminates the need for manual feature engineering, which was a significant bottleneck in traditional machine learning approaches.

Deep learning models are typically trained on massive datasets using powerful computational resources, such as graphics processing units (GPUs). The training process involves adjusting the connections between neurons (weights) to minimize errors in predictions. Once trained, these models can be applied to a wide range of tasks, including image and speech recognition, natural language processing, autonomous driving, and drug discovery.

Key architectures within deep learning include convolutional neural networks (CNNs) for image-related tasks and recurrent neural networks (RNNs) for sequential data like text and time series. More recent advancements include transformer models, which have revolutionized natural language processing. Despite its successes, deep learning still faces challenges related to data requirements, interpretability, and computational cost.