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)

Benchmarking datasets

Image classification, or, for that matter, any classification task, is inherently a supervised learning task. Supervised tasks learn about the different classes through the underlying training sets available.

Even though CNNs are optimized feed forward networks that share weights, the number of parameters to train in a deep ConvNet might be huge. This is one of the reasons why huge training sets are required to achieve better performing networks. Luckily, research groups across the globe have been working towards collecting, hand-annotating, and crowdsourcing different datasets. These datasets are utilized to benchmark performance of different algorithms, as well as to identify winners in different competitions.

The following is a brief listing of widely accepted benchmarking datasets in the field of image classification:

  • ImageNet: With over 14 million hand...