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Neural Networks is a branch of Intelligent Systems Development that focuses on creating artificial systems modeled after the structure and function of the human brain. These networks are composed of layers of interconnected nodes, or "neurons," which process and transmit information. In the context of Deep Learning, Neural Networks can have many hidden layers, allowing them to learn complex patterns and representations from large datasets. Gated Recurrent Units (GRUs) are a type of neural network architecture commonly used in Natural Language Processing and Time Series Prediction tasks. GRUs use gates to selectively allow information from previous time steps to be passed to the current step, making them well-suited for sequential data analysis. Neural Networks fit into the hierarchy by providing a foundation for advanced Artificial Intelligence techniques such as Deep Learning and Gated Recurrent Units. They offer a way to process and analyze large amounts of data in a way that is inspired by the human brain's ability to learn and generalize from experience. By building on this foundation, Intelligent Systems Development can create more advanced, intelligent systems capable of solving complex problems in a wide range of fields.