Deep Learning is a comprehensive guide that explores the foundational and advanced aspects of deep learning, a subfield of machine learning focused on neural networks with multiple layers. The book begins by introducing core concepts such as artificial neural networks, perceptrons, and the principles of training models using techniques like backpropagation and stochastic gradient descent. It covers key architectures including feedforward neural networks, convolutional neural networks (CNNs) for image analysis, and recurrent neural networks (RNNs) for sequential data processing. The text also explores advanced models such as Generative Adversarial Networks (GANs) and autoencoders, highlighting their roles in data generation, compression, and reconstruction. Emphasis is placed on practical applications in fields like computer vision, natural language processing, speech recognition, robotics, and healthcare. Readers are guided through theoretical explanations and real-world implementation using tools like TensorFlow. With a structured and pedagogical approach, this book supports learners and professionals in building a strong conceptual and practical foundation in deep learning.