This chapter introduces the design and implementation of deep neural networks (DNNs) in Rust, focusing on practical approaches for building and training architectures like CNNs, RNNs, and LSTMs. We explore how Rust’s tools and libraries can be leveraged to construct efficient and scalable deep learning models. Through concrete examples, readers will gain insights into how to utilize Rust to create advanced neural network systems while enhancing performance in deep learning tasks.