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)

Image Recognition and Classification

An investment in knowledge always pays the best interest.
– Benjamin Franklin

Image recognition is an active interdisciplinary field of study under the umbrella of computer vision. Image or object recognition, as the name suggests, is the task of identifying objects in an image or video sequence. Traditionally, this field has leveraged advancements in mathematical and computer-aided modeling, and the design of objects. Several hand-annotated datasets have been developed over the years to test and evaluate image recognition systems. Traditional techniques, as we now call them, were dominating the scene and iteratively improving upon the task until recently. In 2012, deep learning arrived at the ImageNet competition and opened the floodgates for rapid improvements and advancements in computer vision and deep learning techniques.

In this...