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Neural networks are computer systems that can learn from large amounts of data and use the knowledge gained to solve various problems. For example, neural networks can be trained to recognize images, process natural language, predict the outcome of financial transactions, and more. Neural networks are computer systems capable of learning from large amounts of data and using the knowledge gained to solve various tasks. For example, neural networks can be trained to recognize images, process natural language, predict the outcomes of financial transactions, and much more. There are many different types of neural networks, each designed to solve specific problems. Some of the most common types include Convolutional Neural Networks, which are used for image processing, and Recurrent Neural Networks, which are used to process data sequences such as natural language. There are many different types of neural networks, each designed to solve specific tasks. Some of the most common types include convolutional neural networks, which are used for image processing, and recurrent neural networks, which are used for processing data sequences, such as natural language. Neural networks work by processing data through layers of neurons. Each layer contains neurons that convert input to output. This output is then passed to the next layer, where it is again processed by the neurons, and so on until the data reaches the output layer. Neural networks work by processing data through layers of neurons. Each layer contains neurons that transform the input data into output data. This output data is then passed to the next layer, where it is again processed by neurons, and so on, until the data reaches the output layer. Neural network training is the process that allows the neural network to learn from examples. During training, the neural network analyzes many examples and tries to identify common patterns between input and output. These patterns are used to solve new problems that were not previously presented to the neural network. Training a neural network is a process that allows the neural network to learn from examples. During training, the neural network analyzes a set of examples and tries to determine the common patterns between input and output data. These patterns are then used to solve new tasks that were not previously presented to the neural network.