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)


A firm foundation and common ground for the understanding of concepts and techniques is very important for any journey. Through this chapter on ML fundamentals, we have tried to achieve precisely this. Before getting started with the basics of deep learning, transfer learning, and more advanced concepts, it is imperative that we form a solid foundation of ML concepts. In this chapter, we have covered quite a bit of ground and provided important pointers to study concepts in more details.

We began the chapter by understanding why machine learning is important and how it is a completely different paradigm. We briefly discussed the relationship between AI, ML, and deep learning. The chapter then moved on to present different ML techniques such as supervised, unsupervised, and reinforcement learning. We discussed in detail which different supervised and unsupervised methods are commonly used.

The chapter also included a quick introduction to the CRISP-DM model for ML project workflows along with ML pipelines. We also discussed EDA of the battles dataset from the fantasyland of Game of Thrones to apply different concepts and learn about the importance of EDA. Toward the end of the chapter, feature extraction and engineering and feature selection were introduced.

In the coming chapters, we will build upon these concepts and eventually apply the learning in chapters concerning different real-world use cases. Welcome onboard!