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

In this chapter, we learned about algorithmic pareidolia in computer vision. We have explained how CNN models can be interpreted by various visualization techniques, like forward pass-based activation visualization, gradient ascent-based filter visualization. Finally, we introduced the DeepDream algorithm, which again, is a slight modification of the gradient ascent-based visualization technique. The DeepDream algorithm is an example of transfer learning being applied to a computer vision or an image-processing task.

We will see more similar applications in the next chapter, which will be focusing on style transfer.