Deep Learning is designed as a textbook for undergraduate and postgraduate students, providing a strong foundation in deep learning concepts. The book begins with fundamental topics such as artificial intelligence, machine learning, natural language processing, image processing, and computer vision, which are essential for understanding deep learning technologies. Core deep learning concepts, including neural networks, activation functions, loss functions, optimization, and regularization, are explored in depth. Additionally, the book introduces data fundamentals, ensuring a complete learning experience. The book covers major deep learning architectures, including Convolutional Neural Networks (CNNs) and Object Detection Networks, with discussions on R-CNN family algorithms, YOLO networks and image segmentation networks. Advanced CNN architectures such as AlexNet, VGGNet, InceptionNet, and ResNet are presented alongside transfer learning applications. The concepts of autoencoders and Recurrent Neural Networks (RNNs), including LSTMs and GRUs, are also introduced. Beyond CNNs, the book also explores Generative AI, covering Large Language Models (LLMs) such as ChatGPT and Generative Adversarial Networks (GANs). It introduces advanced topics like Transformer architectures, along with dedicated chapters on Restricted Boltzmann Machines (RBMs), Deep Belief Networks (DBNs), and Deep Reinforcement Learning algorithms.