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)

Color images

Less than 100 years ago, monochrome capture was a limitation, not a choice. The advent of digital and mobile photography has rendered black and white, or grayscale, images an artistic choice. Surely such images have a dramatic effect, yet there is more to black and white images than just changing an option on the capturing device (be it a digital camera or a phone).

Our understanding of colors and formal color models predates color images. Thomas Young, in 1802, postulated the presence of three types of photoreceptors or cone cells (as shown in the following image). His theory detailed how each one these three cone cells are sensitive to only a particular range of visible light. The theory was further developed to classify these cone cells into short, middle, and long, preferring or blue, green, and red, respectively:

Thomas Young and Hermann Helmholtz: Three Cone...