Hands-On Transfer Learning with Python: Unleash the true power of deep learning using TensorFlow and Keras

Deep learning simplified by transferring prior learning using Python ecosystemAbout This Book* Build deep learning models with transfer learning principles in Python* Learn how to implement transfer learning to solve real-world research problems* Perform complex operations like image captioning, generative deep learning, etc.Who This Book Is ForThis book is primarily targeted towards data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Readers are expected to have basic proficiency in machine learning and Python.What You Will Learn* Setup your own deep learning environment with GPU support.* Delve into transfer learning principles with the help of machine learning and deep learning models* Explore a wide variety of various deep learning architectures, including capsule networks.* Learn about data and network representation, loss functions, etc.* Get to grips with popular pre-trained models & strategies leveraged in transfer learning and get superior model performances* Learn potential challenges in building models from scratch with limited data availability* Explore top real-world research problems around computer vision using transfer learning concepts* Understand how transfer learning can be leveraged in natural language processingIn DetailTransfer learning is a machine learning technique where knowledge gained during training in one set of machine learning problem can be used to train other similar types of problems.The purpose of this book is two-fold, we 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 is on real-world examples and research problems using TensorFlow, Keras and Python ecosystem with hands-on examples.The book starts with core essential concepts of machine learning and deep learning followed by some depiction and coverage of important deep learning architectures such as CNNs, DNNs, RNNs, LSTMs and capsule networks. Our focus then shifts to transfer learning concepts such as VGG, Inception, ResNet and more and how these systems perform better than deep learning models with practical examples. Finally, we focus on a multitude of real-world case studies and problems around areas like computer vision and natural language processing.By the end of this book, you will be all ready to implement both deep learning and transfer learning principles in your own systems.