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)


This chapter focuses on a gentle introduction to the domain of generative deep learning, which has been one of the core ideas at the forefront of true artificial intelligence (AI). We will be focusing on how Convolutional Neural Networks (CNNs) think or visualize patterns in images by leveraging transfer learning. They can generate image patterns never seen before depicting the way these convnets think or even dream! First released by Google in 2015, DeepDream became a viral sensation due to the interesting patterns deep networks started to generate from images. We will be covering the following major topics in this chapter:

  • Motivation—psychological pareidolia
  • Algorithmic pareidolia in computer vision
  • Understanding what CNNs have learned by visualizing internal layers of CNN
  • DeepDream algorithm and how to create your own dream

Just like the previous chapters...