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)

Problem statement

Photographs help us preserve events in time. They don't just help us relive memories but also provide insights into important events from the past. Until color photography became mainstream, our photographic history was captured in black and white. The task of image colorization is to transform a given grayscale image into a plausible color version.

The task of image colorization can be undertaken from different perspectives. The manual process is very time-consuming and requires amazing skills (see the r/Colorization subreddit at https://www.reddit.com/r/Colorization/). Researchers in the field of computer vision and deep learning have been working on different ways of automating the process. Through this chapter, we will work toward understanding how a deep neural network can be leveraged for such a task. We will also try to utilize the power of transfer...