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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Feature selection

The process of feature extraction and engineering helps us extract as well as generate features from underlying datasets. There are cases where this leads to large inputs to an algorithm for processing. It such cases, it is suspected that many of the features in the input might be redundant and may lead to complex models and even overfitting. Feature selection is the process of identifying representative features from the complete feature set that is available/generated. The selected set of features are expected to contain the required information such that the algorithm is able to solve the given task without running into processing, complexity, and overfitting issues. Feature selection also helps in better understanding the data that is being used for the modeling process along with making processing quicker.

Feature selection methods can be broadly classified into the following three categories:

  • Filter methods: As the name suggests, these methods help us rank features based on a statistical score. We then select a subset of these features. These methods are usually not concerned with model outputs, rather evaluating features independently. Threshold based techniques and statistical tests such as correlation coefficients and chi-squared tests are some popular choices.
  • Wrapper methods: These methods perform a comparative search on the performance of different combinations of subsets of features, and then help us select the best performing subset. Backward selection and forward elimination are two popular wrapper methods for feature selection.
  • Embedded methods: These methods provide the best of the preceding two methods by learning which subset of features would be the best. Regularization and tree based methods are popular choices.

Feature selection is an important aspect in the process of building a ML system. It is also one of the major sources of biases that can get into the system if not handled with care. Readers should note that feature selection should be done using a dataset separate from your training dataset. Utilizing the training dataset for feature selection would invariably lead to overfitting, while utilizing the test set for feature selection would overestimate the model's performance.

Most popular libraries provide a wide array of feature selection techniques. Libraries such as scikit-learn provide these methods out of the box. We will see and utilize many of them in subsequent sections/chapters.

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