Tamoghna Ghosh - Hands-On Transfer Learning with Python

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First published: August 2018

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ISBN 978-1-78883-130-7

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This book wouldn't have been possible without several people who made this from a mere concept into reality. I would like to thank my parents, Digbijoy and Sampa, my partner, Durba, my pets, family, and friends for supporting me constantly in my endeavors. A big thank you to the entire team at Packt especially Tushar, Sayli, and Unnati for working tirelessly and supporting us throughout our journey. Also thanks to Matthew Mayo for gracing our book with his foreword and doing great things with KDnuggets.

Thanks to Adrian Rosebrock and PyImageSearch for some excellent visuals and content around pretrained models for computer vision, Federico Baldassarre, Diego Gonzalez-Morin, Lucas Rodes-Guirao, and Emil Wallner for some excellent strategies and implementations for image colorization, Anurag Mishra for giving tips for build an efficient image captioning model, Franois Chollet for building Keras and writing some very useful and engaging content on transfer learning and to the entire Python AI eco-system for helping the community democratize deep learning and artificial intelligence for everyone.

Finally, I would like to thank my managers and mentors Gopalan, Sanjeev, and Nagendra and all my friends and colleagues at Intel for encouraging me and giving me the opportunity to explore new domains in the world of AI. Shoutout also to the folks from Springboard, especially Srdjan Santic for not just giving me an opportunity to learn and interact with some amazing people but also for the passion, zeal, and vision of educating more people on Data Science and AI. Towards Data Science and Ludovic Benistant thanks for helping me learn and share more about AI to the rest of the world and helping me explore cutting-edge research and work in these domains. Last but not the least, I owe a ton of gratitude to my co-authors Raghav and Tamoghna and our reviewer Nitin Panwar for embarking on this journey with me and without whom this book wouldn't have been possible!

Dipanjan Sarkar

I would like to take this opportunity to express gratitude to my parents, Sunil and Neeru, my wife, Swati, my brother, Rajan, family, teachers, friends, colleagues, and mentors who have encouraged, supported and taught me over the years. I would also like to thank my co-authors and good friends Dipanjan Sarkar and Tamoghna Ghosh, for taking me along on this amazing journey. A big thanks to my managers and mentors Vineet, Ravi, and Vamsi along with all my teammates at Optum for their support and encouragement to explore new domains in the Data Science world.

I would like to thank Tushar Gupta, Aaryaman Singh, Sayli Nikalje, Unnati Guha, and Packt for the opportunity and their support throughout this journey. This book wouldn't have been complete without Nitin Panwar's insightful feedback and suggestions. Last but not the least, special thanks to Franois Chollet for Keras, the Python ecosystem and community, fellow authors and researchers who are striving every day to bring these amazing technologies and tools at our fingertips.

Raghav Bali

I would like to thank entire Packt team for giving me this unique opportunity and also guiding me throughout the journey. For this book my co-authors here acted as my mentor as well. They helped me with their insightful suggestions and guidance. Thanks to Nitin for patiently reviewing this book and providing great feedback. I would like to thank my wife Doyel, my son Anurag, and my parents for being a constant source of inspiration for me and tolerating me for working extended hours. Also, I am grateful to my Intel managers for their encouragement and support.

Tamoghna Ghosh

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Foreword

Chances are you are familiar with the recent and seemingly endless machine learning innovations, but do you know about what goes into training a machine learning model? Generally, a given machine learning model is trained on specific data for a particular task. This training process can be exceptionally resource and time-consuming, and since the resulting models are task-specific, the maximum potential of the resulting model is not realized.