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)

Transfer learning strategies

Let's start by first looking at a formal definition for transfer learning and then utilize it to understand different strategies. In their paper, A Survey on Transfer Learning (, Pan and Yang use domain, task, and marginal probabilities to present a framework for understanding transfer learning. The framework is defined as follows:

A domain, D, is defined as a two-element tuple consisting of feature space, , and marginal probability, P(Χ), where Χ is a sample data point.

Here, Χ = {x1, x2....xn} with xi as a specific vector and Χ . Thus:

A task, T, on the other hand, can be defined as a two-element tuple of the label space, γ, and objective function, f. The objective function can also be denoted as P(γ| Χ) from a probabilistic view point...