Book Image

Hands-On Transfer Learning with Python

By : Dipanjan Sarkar, Nitin Panwar, Raghav Bali, Tamoghna Ghosh
Book Image

Hands-On Transfer Learning with Python

By: Dipanjan Sarkar, Nitin Panwar, 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.

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Table of Contents (14 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Machine Learning Fundamentals
Machine Learning Fundamentals
Why ML?
ML techniques
CRISP-DM
Standard ML workflow
Exploratory data analysis
Feature extraction and engineering
Feature selection
Summary
2
Deep Learning Essentials
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
3
Understanding Deep Learning Architectures
Understanding Deep Learning Architectures
Neural network architecture
Various architectures
Summary
4
Transfer Learning Fundamentals
Transfer Learning Fundamentals
Introduction to transfer learning
Transfer learning strategies
Transfer learning and deep learning
Deep transfer learning types
Challenges of transfer learning
Summary
5
Unleashing the Power of Transfer Learning
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
6
Image Recognition and Classification
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
7
Text Document Categorization
Text Document Categorization
Text categorization
Word representations
CNN document model
Summary
8
Audio Event Identification and Classification
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
9
DeepDream
DeepDream
Introduction
DeepDream
Summary
10
Style Transfer
Style Transfer
Understanding neural style transfer
Image preprocessing methodology
Building loss functions
Constructing a custom optimizer
Style transfer in action
Summary
11
Automated Image Caption Generator
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
12
Image Colorization
Image Colorization
Problem statement
Color images
Building a coloring deep neural network
Challenges
Further improvements
Summary
13
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Benchmarking datasets

Image classification, or, for that matter, any classification task, is inherently a supervised learning task. Supervised tasks learn about the different classes through the underlying training sets available.

Even though CNNs are optimized feed forward networks that share weights, the number of parameters to train in a deep ConvNet might be huge. This is one of the reasons why huge training sets are required to achieve better performing networks. Luckily, research groups across the globe have been working towards collecting, hand-annotating, and crowdsourcing different datasets. These datasets are utilized to benchmark performance of different algorithms, as well as to identify winners in different competitions.

The following is a brief listing of widely accepted benchmarking datasets in the field of image classification:

  • ImageNet: With over 14 million hand...
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