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)


DeepDream is an artistic image modification technique that leverages the representations learned by deep CNN code Inception, named after the movie of the same name. We can take any input image and process it to generate trippy pictures, full of algorithmic pareidolia artifacts, bird feathers, dog-like faces, dog eyes—a by-product of the fact that the DeepDream convent was trained on ImageNet, where dog breeds and bird species are vastly over-represented.

The DeepDream algorithm is almost identical to the ConvNet filter visualization technique using gradient ascent, except in a few differences as follows:

  • In DeepDream, activation of entire layers is maximized, whereas in visualization only a specific filter is maximized, thus mixing together visualizations of large numbers of features maps
  • We start not from a random noise input, but rather from an existing image...