A hands-on guide to building and deploying Deep Learning models with Python. “Deep Learning for Data Architects” is a comprehensive guide that bridges the gap between data architecture and Deep Learning. It provides a solid foundation in Python for data science and serves as a launchpad into the world of AI and Deep Learning. The book begins by addressing the challenges of transforming raw data into actionable insights. It provides a practical understanding of data handling and covers the construction of neural network-based predictive models. The book then explores specialized networks such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). The book delves into the theory and practical aspects of these networks and offers Python code implementations for each. The final chapter of the book introduces Transformers, a revolutionary model that has had a significant impact on natural language processing (NLP). This chapter provides you with a thorough understanding of how Transformers work and includes Python code implementations. This book is for anyone who is interested in a career in emerging technologies, such as Artificial Intelligence (AI), data analytics, Machine Learning, Deep Learning, and Data Science.