süvaõppimise

Süvaõppimine, also known as deep learning, is a subset of machine learning that uses artificial neural networks with multiple layers to learn from data. These networks are inspired by the structure and function of the human brain. Unlike traditional machine learning algorithms that often require manual feature extraction, deep learning models can automatically discover and learn hierarchical representations of data. This means that as data passes through the layers of the network, increasingly complex and abstract features are identified.

The "deep" in deep learning refers to the number of layers in the neural network. A shallow network might have only one or two hidden layers, whereas a deep network can have tens, hundreds, or even thousands of layers. Each layer transforms the input data into a more abstract representation, which is then passed to the next layer. This layered approach allows deep learning models to tackle complex problems such as image recognition, natural language processing, and speech synthesis with remarkable accuracy.

Key components of deep learning architectures include neurons, which are computational units that process information, and connections between neurons, which have associated weights that are adjusted during the training process. Training a deep learning model involves feeding it large amounts of data and iteratively adjusting the weights to minimize errors in its predictions. This process often requires significant computational resources and specialized hardware like GPUs. Deep learning has led to significant advancements in various fields, driving innovation in areas like autonomous vehicles, medical diagnosis, and personalized recommendations.