Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras

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Deep Learning Essentials

This chapter provides a whirlwind tour of deep learning essentials, starting from the very basics of what deep learning really means, and then moving on to other essential concepts and terminology around neural networks. The reader will be given an overview of the basic building blocks of neural networks, and how deep neural networks are trained. Concepts surrounding model training, including activation functions, loss functions, backpropagation, and hyperparameter-tuning strategies will be covered. These foundational concepts will be of great help for both beginners and experienced data scientists who are venturing into deep neural network models. Special focus has been given to how to set up a robust cloud-based deep learning environment with GPU support, along with tips for setting up an in-house deep learning environment. This should be very useful...

Key benefits

Build deep learning models with transfer learning principles in Python

implement transfer learning to solve real-world research problems

Perform complex operations such as image captioning neural style transfer

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.

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

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