Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

Description

Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. 1 Machine Learning Fundamentals 2 Deep Learning Essentials 3 Understanding Deep Learning Architectures 4 Transfer Learning Fundamentals 5 Unleash the Power of Transfer Learning 6 Image Recognition and Classification 7 Text Document Categorization 8 Audio Identification and Categorization 9 Deep Dream 10 Neural Style Transfer 11 Automated Image Caption Generator 12 Image Colorization