This was definitely one of the toughest real-world problems we tackled in the entire book. It was a perfect combination of transfer learning and generative deep learning being applied on a combination of data from images and text that combine different domains around computer vision and NLP. We covered essential concepts around understanding image captioning, the major components needed to build a caption generator, and built our own model from scratch. We made effective use of transfer learning principles by leveraging pretrained computer vision models to extract the right features from images to be captioned and then coupled them with some sequential models, such as LSTMs, to generate captions. The efficient and effective evaluation of sequential models is tough and we leveraged the industry standard BLEU score metric for our purpose. We implemented a scoring function...
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Book Overview & Buying
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Table Of Contents
Hands-On Transfer Learning with Python
By :
Hands-On Transfer Learning with Python
By:
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)
Preface
Free Chapter
Machine Learning Fundamentals
Deep Learning Essentials
Understanding Deep Learning Architectures
Transfer Learning Fundamentals
Unleashing the Power of Transfer Learning
Image Recognition and Classification
Text Document Categorization
Audio Event Identification and Classification
DeepDream
Style Transfer
Automated Image Caption Generator
Image Colorization
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