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

The intent of this chapter was to give you a more hands-on perspective of building deep learning models to solve a real-world problem, and also to understand the effectiveness of transfer learning. We covered aspects of the need for transfer learning, especially when solving problems with the constraints of limited data. We built several CNN models from scratch and also saw the benefits of a proper image augmentation strategy. We also looked at how to leverage pretrained models for transfer learning and covered the various ways to use them, including as feature extractors, as well as fine-tuning. We saw the detailed architecture of the VGG-16 model and how to leverage the model as an efficient image feature extractor. Strategies pertaining to transfer learning, including feature extraction and fine-tuning, along with image augmentation, were used to build effective deep...