süvaõppe

Süvaõppe, commonly known as deep learning, is a subfield of machine learning that focuses on algorithms inspired by the structure and function of the human brain, specifically artificial neural networks. These networks consist of multiple layers of interconnected nodes, or artificial neurons, that process and transform data. The "deep" in deep learning refers to the numerous layers these networks can have, allowing them to learn hierarchical representations of data.

In deep learning, information passes through successive layers, with each layer extracting increasingly complex features from the input. For example, in image recognition, early layers might detect edges and simple shapes, while deeper layers combine these to recognize more intricate objects like faces or cars. This hierarchical learning approach enables deep learning models to automatically discover relevant features from raw data, eliminating the need for manual feature engineering that was common in traditional machine learning.

The effectiveness of deep learning relies heavily on large datasets and significant computational power, often provided by Graphics Processing Units (GPUs). This has led to breakthroughs in various domains, including natural language processing (e.g., machine translation, text generation), computer vision (e.g., object detection, image segmentation), and speech recognition. While powerful, deep learning models can be computationally expensive to train and may require careful tuning of hyperparameters.