mélytanulással

Mélytanulás, often translated as deep learning, is a subfield of machine learning that uses artificial neural networks with multiple layers to learn from data. These networks are designed to mimic the structure and function of the human brain, allowing them to process complex patterns and extract meaningful features from raw data. Unlike traditional machine learning algorithms that require manual feature engineering, deep learning models can automatically learn hierarchical representations of data.

The "deep" in deep learning refers to the depth of the neural network, meaning it has many layers. Each layer transforms the input data into a more abstract and complex representation. For example, in image recognition, early layers might detect edges and simple shapes, while deeper layers can identify objects and their components. This layered approach enables deep learning models to tackle tasks that were previously difficult or impossible for machines, such as natural language processing, speech recognition, and computer vision.

Deep learning has achieved remarkable success in various applications. It powers facial recognition systems, recommendation engines on streaming platforms, autonomous driving technology, and medical image analysis. The effectiveness of deep learning relies heavily on the availability of large datasets and significant computational power, often utilizing specialized hardware like GPUs (Graphics Processing Units) for training. Despite its successes, deep learning still faces challenges related to interpretability, data requirements, and computational cost.