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)

CNN document model

We previously saw how word embeddings are capable of capturing many semantic relationships between the concepts they represent. We will now introduce a ConvNet document model that builds hierarchical distributed representations of documents. This was published in the paper https://arxiv.org/pdf/1406.3830.pdf by Misha Denil et al. The model is divided into two levels, a sentence level and a document level, both of which are implemented using ConvNets. At the sentence level, a ConvNet is used to transform embeddings for the words in each sentence into an embedding for the entire sentence. At the document level, another ConvNet is used to transform sentence embeddings to a document embedding.

In any ConvNet architecture a convolution layer is followed by a sub-sampling/pooling layer. Here, we use k-max pooling. A k-max pooling operation is slightly different from...