Preface

With the world moving towards digitization and automation, as a technologist/programmer it is important to keep oneself updated and learn how to leverage these tools and techniques. This book, Hands-On Transfer Learning with Python, is an attempt to help practitioners get acquainted with and equipped to use these advancements in their respective domains. This book is structured broadly into three sections:

• Deep learning foundations
• Essentials of transfer learning
• Transfer learning case studies

Transfer learning is a machine learning (ML) technique where knowledge gained during the training of one set of ML problems can be used to train other similar types of problems.

The purpose of this book is two-fold. We will focus on detailed coverage of deep learning and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus will be on real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples.

The book starts with core essential concepts of ML and deep learning, followed by some depictions and coverage of important deep learning architectures, such as CNNs, DNNs, RNNs, LSTMs, and capsule networks. Our focus then shifts to transfer learning concepts and pretrained state of the art networks such as VGG, Inception, and ResNet. We also learn how these systems can be leveraged to improve performance of our deep learning models. Finally, we focus on a multitude of real-world case studies and problems in areas such as computer vision, audio analysis, and natural language processing (NLP).

By the end of this book, you will be all ready to implement both deep learning and transfer learning principles in your own systems.