I hope every reader enjoys this voyage in Deep Learning and find their adventure. Think of Deep Learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends Deep Learning. In this spirit, this book presents the Deep Learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified. This book is appropriate for graduate and Ph.D. students, as well as researchers and practitioners. To the practitioners, the book provides complete illustrative implementations to use in developing solutions. For researchers, the book has several research directions and implementations of new ideas, e.g., custom activations, regularizations, and multiple pooling. In graduate programs, this book is suitable for a one-semester introductory course in deep learning. The first three chapters introduce the field, a working example, and sets up a student with TensorFlow. Additionally, the book illustrates how to implement Deep Learning networks with TensorFlow in Python.