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.

Preface

Who this book is for

What this book covers

To get the most out of this book

Free Chapter

Machine Learning Fundamentals

Machine Learning Fundamentals

Standard ML workflow

Exploratory data analysis

Feature extraction and engineering

Feature selection

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

Understanding Deep Learning Architectures

Understanding Deep Learning Architectures

Neural network architecture

Various architectures

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

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

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

Text Document Categorization

Text Document Categorization

Text categorization

Word representations

CNN document model

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

DeepDream

Style Transfer

Understanding neural style transfer

Image preprocessing methodology

Building loss functions

Constructing a custom optimizer

Style transfer in action

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!

Image Colorization

Image Colorization

Problem statement

Building a coloring deep neural network

Further improvements

Other Books You May Enjoy

Other Books You May Enjoy

Leave a review - let other readers know what you think

Customer Reviews

5 star

0

4 star

0

3 star

0

2 star

0

1 star

0

What is deep learning?

In machine learning (ML), we try to automatically discover rules for mapping input data to a desired output. In this process, it's very important to create appropriate representations of data. For example, if we want to create an algorithm to classify an email as spam/ham, we need to represent the email data numerically. One simple representation could be a binary vector where each component depicts the presence or absence of a word from a predefined vocabulary of words. Also, these representations are task-dependent, that is, representations may vary according to the final task that we desire our ML algorithm to perform.

In the preceding email example, instead of identifying spam/ham if we want to detect sentiment in the email, a more useful representation of the data could be binary vectors where the predefined vocabulary consists of words with positive...