Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh

Overview of this book

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.
Table of Contents (14 chapters)

Summary

Image colorization is one of the leading-edge topics from the deep learning world. As our understanding of transfer learning and deep learning is maturing, the application scope is getting exciting and more creative. Image colorization is an active area of research and lately some exciting work has been shared by deep learning experts.

In this chapter, we learned about color theory, different color models, and color spaces. This understanding helped us reformulate the problem statement to that of mapping from a single-channel grayscale image to a two-channel output. We then worked toward building a colornet based on the works of Baldassarre and his co-authors. The implementation involved a unique three-layer network consisting of an encoder, a decoder, and a fusion layer. The fusion layer allowed us to utilize transfer learning by concatenating VGG16 embeddings with the...