This chapter provides a structured introduction to deep learning, a subfield of machine learning that has driven major advances in artificial intelligence. It begins with foundational preliminaries, covering the mathematical and computational principles underlying deep learning models. The chapter then explores core algorithms, including backpropagation, gradient descent, and various neural network architectures. Advanced deep learning architectures—such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and generative models—are discussed in detail, highlighting their design principles and use cases. Practical guidance is provided on building and implementing deep learning projects, including data preparation, model training, evaluation, and deployment. Finally, it addresses strategic considerations in deep learning development.