This chapter presents a comprehensive examination of Deep Learning (DL), beginning with the foundational principles and progressing to more advanced models and techniques. It starts with an exploration of neural networks, emphasizing the biological inspirations underlying their development, and proceeds to an in-depth study of Multilayer Perceptrons (MLPs), which form the cornerstone of deep learning. Key topics include various activation functions, downstream tasks, and the critical processes involved in training neural network models through forward and backward propagation. Additionally, the chapter explores a variety of neural network architectures, including Convolutional Neural Networks (CNNs), Graph Neural Networks (GNNs), and Recurrent Neural Networks (RNNs), offering a detailed understanding of their structure and functionality. The discussion extends to cutting-edge topics such as Generative AI (GenAI) and Large Language Models (LLMs), providing both theoretical foundations and practical applications. Furthermore, emerging areas, including privacy-preserving machine learning techniques such as Federated Learning (FL) and Differential Privacy, are examined. By integrating both theoretical insights and real-world applications, this chapter serves as an essential resource for students, researchers, and professionals seeking a comprehensive understanding of the dynamic and rapidly evolving field of Artificial Intelligence.