This book focuses on Deep Learning (DL), which is an important aspect of Data Science, that includes predictive modeling. DL applications are widely used in domains such as finance, transport, healthcare, automanufacturing, and advertising. The design of the DL models based on artificial neural networks is influenced by the structure and operation of the brain. This book presents a comprehensive resource for those who seek a solid grasp of the techniques in DL. Deep Learning (DL) is an important and evolving area in data science that includes statistics and predictive modeling. It is concerned with algorithms inspired by the brain’s structure and functions known as artificial neural networks. DL can automatically learn features in data, by updating learned weights at each layer. This book provides adequate theoretical coverage of DL techniques and applications. This book will teach deep learning concepts from scratch. We aim to make DL approachable by teaching the concepts and theories behind DL models. Thus, practitioners can grab the critical thinking skills required to formulate problems, design and develop models to make accurate predictions and support the decision- making process.