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Neural networks, a crucial component of deep learning, are algorithms that mimic the workings of the human brain to "learn" from data. They consist of interconnected layers of nodes, or artificial neurons, which process and transmit information. The first layer is the input layer, which receives raw data. The final layer is the output layer, which produces a prediction or decision based on the processed data. Between them are hidden layers that extract increasingly abstract features from the input data. Neural networks enable computers to perform complex tasks without explicit programming, such as image recognition and natural language processing. They can learn patterns in vast amounts of data, improving their performance over time. This ability to improve through experience is what makes neural networks so powerful for deep learning applications. In the hierarchy of science, computer science, and computer systems, neural networks fall under deep learning, which itself is a subset of computer systems. Deep learning uses neural networks to analyze and learn from data, making them an essential component of this rapidly growing field. Neural networks represent a significant advancement in artificial intelligence, enabling computers to perform tasks that were previously only possible for humans.