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Machine Learning with TensorFlow 1.x

You're reading from Machine Learning with TensorFlow 1.x Second generation machine learning with Google's brainchild - TensorFlow 1.x

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781786462961

Length 304 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Machine Learning

Authors (3):

Hua

Ahmed

Ul Azeem

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Started with TensorFlow FREE CHAPTER

2. Your First Classifier

3. The TensorFlow Toolbox

4. Cats and Dogs

5. Sequence to Sequence Models-Parlez-vous Français?

6. Finding Meaning

7. Making Money with Machine Learning

8. The Doctor Will See You Now

9. Cruise Control - Automation

10. Go Live and Go Big

11. Going Further - 21 Problems

12. Advanced Installation

Setting up the project

In this chapter, we will fine-tune a VGG model that has been trained on ImageNet data with 1,000 classes. We have provided an initial project with a pretrained VGG model and some utility files. You can go ahead and download the code from https://github.com/mlwithtf/mlwithtf/tree/master/chapter_09.

In the folder chapter-09, you will have the following structure:

- data--VGG16.npz- samples_data- production- utils--__init__.py--debug_print.py- README.md

There are two files that you should understand:

VGG16.npz is the pre-trained model that is exported from the Caffe model. Chapter 11, Going Further - 21 Problems will show you how to create this file from the Caffe model. In this chapter, we will use this as the initial values for our model. You can download this file from the README.md in the chapter_09 folder.
production is the Flask server that we created to serve as a web interface for users to upload and correct the model.
debug_print.py contains some methods that we will...

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Authors (3)

Hua

Hua

Quan Hua is a Computer Vision and Machine Learning Engineer at BodiData, a data platform for body measurements, where he focuses on developing computer vision and machine learning applications for a handheld technology capable of acquiring a body avatar while a person is fully clothed. He earned a bachelor of science degree from the University of Science, Vietnam, specializing in Computer Vision. He has been working in the field of computer vision and machine learning for about 3 years at start-ups. Quan has been writing for Packt since 2015 for a Computer Vision book, OpenCV 3 Blueprints.

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Ahmed

Ahmed

Saif Ahmed is an accomplished quantitative analyst and data scientist with 15 years of industry experience. His career started in management consulting at Accenture and lead him to quantitative and senior management roles at Goldman Sachs and AIG Investments. Most recently, he co-founded and runs a start-up focused on applying Deep Learning to automating medical imaging. He obtained his bachelor's degree in computer science from Cornell University and is currently pursuing a graduate degree in data science at U.C. Berkeley.

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Ul Azeem

Ul Azeem

Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, hes pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.

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