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)

Understanding the data

Let's take a look at the data we will be using to build our model. To keep things simple, we will be using the Flickr8K dataset. This dataset includes images obtained from Flickr, a popular image sharing website. To download the dataset, you can request it by filling in a form at https://forms.illinois.edu/sec/1713398 from the Department of Computer Science, University of Illinois, and you should get the download link in your email.

To check out the details pertaining to each image, you can refer to their website, http://nlp.cs.illinois.edu/HockenmaierGroup/8k-pictures.html, which talks about each image, its source, and five text-based captions for each image. In general, any sample image would have several captions similar to the following:

You can clearly see the image and its corresponding captions. It is quite evident that all the captions try...