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Hands-On Transfer Learning with Python

You're reading from Hands-On Transfer Learning with Python Implement advanced deep learning and neural network models using TensorFlow and Keras

Product type Paperback

Published in Aug 2018

Publisher Packt

ISBN-13 9781788831307

Length 438 pages

Edition 1st Edition

Languages

Python

Tools

Keras

Concepts

Deep Learning

Authors (4):

Nitin Panwar

Sarkar

Raghav Bali

Tamoghna Ghosh

View More author details

Table of Contents (20) Chapters

Title Page

Dedication

Packt Upsell

Foreword

Contributors

Preface

1. Machine Learning Fundamentals FREE CHAPTER

2. Deep Learning Essentials

3. Understanding Deep Learning Architectures

4. Transfer Learning Fundamentals

5. Unleashing the Power of Transfer Learning

6. Image Recognition and Classification

7. Text Document Categorization

8. Audio Event Identification and Classification

9. DeepDream

10. Style Transfer

11. Automated Image Caption Generator

12. Image Colorization

1. Other Books You May Enjoy

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Index

Building a vocabulary for our captions

The next step involves doing some preprocessing for our caption data and building a vocabulary or metadata dictionary for our captions. We start by reading in our training dataset records and writing a function to preprocess our text captions:

train_df = pd.read_csv('image_train_dataset.tsv', delimiter='\t') 
total_samples = train_df.shape[0] 
total_samples 
 
35000 
 
# function to pre-process text captions 
def preprocess_captions(caption_list): 
    pc = [] 
    for caption in caption_list: 
        caption = caption.strip().lower() 
        caption = caption.replace('.', '').replace(',',   
                      '').replace("'", "").replace('"', '') 
        caption = caption.replace('&','and').replace('(','').replace(')',   
                                         '').replace('-', ' ') 
        caption = ' '.join(caption.split())  
        caption = '<START> '+caption+' <END>' 
        pc.append(caption) 
    return pc

We will now...

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Authors (4)

Nitin Panwar

Nitin Panwar

See other products by Nitin Panwar

Sarkar

Sarkar

Dipanjan (DJ) Sarkar is a Data Scientist at Intel, leveraging data science, machine learning, and deep learning to build large-scale intelligent systems. He holds a master of technology degree with specializations in Data Science and Software Engineering. He has been an analytics practitioner for several years now, specializing in machine learning, NLP, statistical methods, and deep learning. He is passionate about education and also acts as a Data Science Mentor at various organizations like Springboard, helping people learn data science. He is also a key contributor and editor for Towards Data Science, a leading online journal on AI and Data Science. He has also authored several books on R, Python, machine learning, NLP, and deep learning.

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Raghav Bali

Raghav Bali

Raghav Bali is a Staff Data Scientist at Delivery Hero, a leading food delivery service headquartered in Berlin, Germany. With 12+ years of expertise, he specializes in research and development of enterprise-level solutions leveraging Machine Learning, Deep Learning, Natural Language Processing, and Recommendation Engines for practical business applications. Besides his professional endeavors, Raghav is an esteemed mentor and an accomplished public speaker. He has contributed to multiple peer-reviewed papers and authored multiple well received books. Additionally, he holds co-inventor credits on multiple patents in healthcare, machine learning, deep learning, and natural language processing.

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Tamoghna Ghosh

Tamoghna Ghosh

Tamoghna Ghosh is a machine learning engineer at Intel Corporation. He has overall 11 years of work experience including 4 years of core research experience at Microsoft Research (MSR) India. At MSR he worked as a research assistant in cryptanalysis of block ciphers. His technical expertise's are in big data, machine learning, NLP, information retrieval, data visualization and software development. He received M.Tech (Computer Science) degree from the Indian Statistical Institute, Kolkata and M.Sc (Mathematics) from University of Calcutta with specialization in functional analysis and mathematical modeling/dynamical systems. He is passionate about teaching and conducts internal training in data science for Intel at various levels.

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