Süvaõppel

Süvaõppel, known in English as deep learning, is a subfield of machine learning that uses artificial neural networks with multiple layers to learn and represent data. These multi-layered networks, often referred to as deep neural networks, allow for the extraction of progressively more complex features from raw data. Unlike traditional machine learning algorithms that often require manual feature engineering, deep learning models can learn hierarchical representations directly from the data itself.

The core idea behind deep learning is to mimic the structure and function of the human brain's neural networks. Each layer in a deep neural network processes the output from the previous layer, transforming it into a more abstract representation. This allows the model to learn intricate patterns and relationships within data, making it highly effective for tasks such as image recognition, natural language processing, speech recognition, and recommendation systems.

The development of deep learning has been significantly fueled by the availability of large datasets and advancements in computational power, particularly with the use of GPUs. Training deep learning models can be computationally intensive, but the ability to achieve state-of-the-art performance on various complex tasks has led to its widespread adoption across many industries and research areas.