Süvaõppemudelid

Süvaõppemudelid, often translated as deep learning models, represent a powerful class of machine learning algorithms inspired by the structure and function of the human brain. They are characterized by having multiple layers of artificial neural networks, which allows them to learn hierarchical representations of data. Each layer in a deep learning model extracts progressively more complex features from the input. For instance, in image recognition, early layers might detect edges and simple shapes, while deeper layers combine these to identify objects and scenes.

The "deep" in deep learning refers to this depth of layers. Traditional machine learning models typically have fewer layers and often require manual feature engineering, meaning humans need to explicitly tell the model what features are important. Deep learning models, however, excel at automatically discovering and learning these relevant features directly from raw data, such as pixels in an image or words in a text.

Key architectures within deep learning include Convolutional Neural Networks (CNNs), which are particularly effective for image and video analysis, and Recurrent Neural Networks (RNNs) and their variants like Long Short-Term Memory (LSTM) networks, which are well-suited for sequential data such as text and time series. Transformers, another significant architecture, have revolutionized natural language processing and are increasingly used in other domains.

The training of deep learning models involves feeding large amounts of data through the network and adjusting the model's internal parameters (weights and biases) using optimization algorithms, most commonly backpropagation. This process allows the model to minimize errors and improve its predictive accuracy. Due to their data hunger and computational intensity, deep learning models typically require significant computational resources, often utilizing GPUs for accelerated training.