Deep learning

Deep learning is a branch of machine learning focused on creating and training artificial neural networks with multiple layers. These neural networks are able to automatically extract important and complex characteristics from the input data, just as they would learn themselves, and use these characteristics to solve problems. Thanks to this ability, deep learning can successfully solve complex problems and work effectively with data that contains a large amount of information.

The principle of operation
The job of deep learning is to sequentially transfer data through layers of a neural network, followed by adjusting weights and parameters so that the model can detect complex patterns and patterns in the data. After training, the network can be used to predict or classify new data.

Types of neural networks
Deep learning covers the use of neural networks with a large number of layers. Neural networks, in turn, are part of the deep learning toolkit.

Each type of neural network specializes in certain types of data and tasks and can be applied in various fields and scenarios.

  • Convolutional neural networks (CNNs) process and analyze data with spatial structure. They are used in computer vision, image recognition and video.
  • Recurrent neural networks (RNNs) work with sequential data. They are used for machine translation, natural language processing, and text generation tasks.
  • Recurrent convolutional neural networks (RCNNS) combine the properties of convolutional neural networks and recurrent neural networks. They are used for tasks that combine sequence processing and spatial data structure.
  • Autoencoders aim to compress input data into a more compact representation and then restore it back from that representation. Autoencoders help you explore hidden data structures, reduce data dimensionality, and generate new examples.
  • Generative adversarial networks (GANS) consist of two competing neural networks — a generator and a discriminator. The generator creates new data that could deceive the discriminator, and the discriminator tries to distinguish real data from fake data. GAN is used to generate content.
  • Transformers are based on attention mechanisms. They are used to process sequential data such as texts and time series sequences.

Areas of application of deep learning
Deep learning is used in various fields due to its ability to learn from a large amount of data and make accurate predictions. Deep learning is used in autonomous driving to navigate cars, in healthcare to diagnose diseases, in e-commerce to recommend products, and in the gaming industry for a more realistic gameplay.

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