Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras
Authors: Dipanjan Sarkar – Raghav Bali – Tamoghna Ghosh
ISBN-10: 1788831306
ISBN-13: 9781788831307
Released: 2018-08-31
Print Length 页数: 438 pages
Book Description
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.
Contents
1: MACHINE LEARNING FUNDAMENTALS
2: DEEP LEARNING ESSENTIALS
3: UNDERSTANDING DEEP LEARNING ARCHITECTURES
4: TRANSFER LEARNING FUNDAMENTALS
5: UNLEASHING THE POWER OF TRANSFER LEARNING
6: IMAGE RECOGNITION AND CLASSIFICATION
7: TEXT DOCUMENT CATEGORIZATION
8: AUDIO EVENT IDENTIFICATION AND CLASSIFICATION
9: DEEPDREAM
10: STYLE TRANSFER
11: AUTOMATED IMAGE CAPTION GENERATOR
12: IMAGE COLORIZATION
What You Will Learn
Set up your own DL environment with graphics processing unit (GPU) and Cloud support
Delve into transfer learning principles with ML and DL models
Explore various DL architectures,including CNN,LSTM,and capsule networks
Learn about data and network representation and loss functions
Get to grips with models and strategies in transfer learning
Walk through potential challenges in building complex transfer learning models from scratch
Explore real-world research problems related to computer vision and audio analysis
Understand how transfer learning can be leveraged in NLP
Authors
Dipanjan Sarkar
Dipanjan (DJ) Sarkar is a Data Scientist at Intel,leveraging data science,machine learning,and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now,specializing in machine learning,NLP,statistical methods,and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard,helping people learn data science. He is also a key contributor and editor for Towards Data Science,a leading online journal on AI and Data Science. He has also authored several books on R,Python,machine learning,NLP,and deep learning.
Raghav Bali
Raghav Bali is a Data Scientist at Optum (United Health Group). His work involves research & development of enterprise level solutions based on Machine Learning,Deep Learning and Natural Language Processing for Healthcare & Insurance related use cases. In his previous role at Intel,he was involved in enabling proactive data driven IT initiatives. He has also worked in ERP and finance domains with some of the leading organizations in the world. Raghav has also authored multiple books with leading publishers. Raghav has a master’s degree (gold medalist) in Information Technology from International Institute of Information Technology,Bangalore. Raghav loves reading and is a shutterbug capturing moments when he isn’t busy solving problems.
