Book Image

Hands-On Transfer Learning with Python

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

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, 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

We covered a whole lot of theory in the first two parts of the book. Having built a strong foundation of concepts and techniques, we started the use case-driven journey in this chapter. This chapter is the first in a series of upcoming chapters to showcase actual use cases of transfer learning in different scenarios and domains. In this chapter, we applied transfer learning to the domain of visual object identification, or, as it is popularly termed, image classification.

We started off with a quick refresher around CNNs and how the whole stage of computer-aided object identification changed once and for all with the arrival of deep learning models in 2012. We briefly touched upon various state-of-the-art image classification models, which have surpassed human performance. We also had a quick look into different benchmarking datasets utilized by academic and industry experts...