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Hands-On Transfer Learning with Python

By : Nitin Panwar, Sarkar, Raghav Bali, Tamoghna Ghosh

4 (3)

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Hands-On Transfer Learning with Python

4 (3)

By: Nitin Panwar, Sarkar, Raghav Bali, Tamoghna Ghosh

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Free Chapter

Machine Learning Fundamentals

Why ML?

ML techniques

CRISP-DM

Standard ML workflow

Exploratory data analysis

Feature extraction and engineering

Feature selection

Summary

Deep Learning Essentials

What is deep learning?

Deep learning frameworks

Setting up a cloud-based deep learning environment with GPU support

Setting up a robust, on-premise deep learning environment with GPU support

Neural network basics

Summary

Understanding Deep Learning Architectures

Neural network architecture

Various architectures

Summary

Transfer Learning Fundamentals

Introduction to transfer learning

Transfer learning strategies

Transfer learning and deep learning

Deep transfer learning types

Challenges of transfer learning

Summary

Unleashing the Power of Transfer Learning

The need for transfer learning

Building CNN models from scratch

Leveraging transfer learning with pretrained CNN models

Evaluating our deep learning models

Summary

Image Recognition and Classification

Deep learning-based image classification

Benchmarking datasets

State-of-the-art deep image classification models

Image classification and transfer learning

Summary

Text Document Categorization

Text categorization

Word representations

CNN document model

Summary

Audio Event Identification and Classification

Understanding audio event classification

Exploratory analysis of audio events

Feature engineering and representation of audio events

Audio event classification with transfer learning

Building a deep learning audio event identifier

Summary

DeepDream

Introduction

DeepDream

Summary

Style Transfer

Understanding neural style transfer

Image preprocessing methodology

Building loss functions

Constructing a custom optimizer

Style transfer in action

Summary

Automated Image Caption Generator

Understanding image captioning

Formulating our objective

Understanding the data

Approach to automated image captioning

Image feature extraction with transfer learning

Building a vocabulary for our captions

Building an image caption dataset generator

Building our image language encoder-decoder deep learning model

Training our image captioning deep learning model

Evaluating our image captioning deep learning model

Automated image captioning in action!

Summary

Image Colorization

Problem statement

Color images

Building a coloring deep neural network

Challenges

Further improvements

Summary

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Summary

This chapter covered various advances in neural network architectures and their application to a varied set of real-world problems. We discussed the need for these architectures and why a simple deep multilayer neural network won't sufficiently solve all sorts of problems, given that it has great expressive power and a rich hypothesis space. Many of these architectures discussed will be used in later chapters when covering transfer learning use cases. References to the Python code for almost all the architectures is provided. We have also tried to clearly explain some of the very recent architectures, such as CapsNet, MemNNs, and NTMs. We will be frequently referring back to this chapter while walking you through the transfer learning use cases.

The next chapter will introduce the concepts of transfer learning.