Hands-On Convolutional Neural Networks with TensorFlow

This is the code repository for [Hands-On Convolutional Neural Networks with TensorFlow](Packt UTM URL of the Book), published by Packt.

Solve computer vision problems with modeling in TensorFlow and Python

What is this book about?

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time!

This book covers the following exciting features:

Train machine learning models with TensorFlow
Create systems that can evolve and scale during their life cycle
Use CNNs in image recognition and classification
Use TensorFlow for building deep learning models
Train popular deep learning models

If you feel this book is for you, get your copy today!

Instructions and Navigations

All of the code is organized into folders. For example, Chapter02.

The code will look like the following:

## Installing Python (Prefered 3.n)

sudo apt-get install python3-pip python3-dev python-virtualenv
sudo pip install -U pip

## Packages to install
## If you already have python 2.7 installed you may refer pip as pip3

sudo pip install numpy
sudo pip install matplotlib
sudo pip install fire
sudo pip install -U tensorflow
sudo pip install jupyter

Following is what you need for this book: This book is for Software Engineers, Data Scientists, or Machine Learning practitioners who want to use CNNs for solving real-world problems. Knowledge of basic machine learning concepts, linear algebra and Python will help.

With the following software and hardware list you can run all code files present in the book (Chapter 1-15).

Software and Hardware List

Chapter	Software required	OS required
1-9	Python 3, TensorFlow	Windows, Mac OS X, and Linux (Any)

Get to Know the Authors

Iffat Zafar was born in Pakistan. She received her Ph.D. from the Loughborough University in Computer Vision and Machine Learning in 2008. After her Ph.D. in 2008, she worked as research associate at the Department of Computer Science, Loughborough University, for about 4 years. She currently works in the industry as an AI engineer, researching and developing algorithms using Machine Learning and Deep Learning for object detection and general Deep Learning tasks for edge and cloud-based applications.

Giounona Tzanidou is a PhD in computer vision from Loughborough University, UK, where she developed algorithms for runtime surveillance video analytics. Then, she worked as a research fellow at Kingston University, London, on a project aiming at prediction detection and understanding of terrorist interest through intelligent video surveillance. She was also engaged in teaching computer vision and embedded systems modules at Loughborough University. Now an engineer, she investigates the application of deep learning techniques for object detection and recognition in videos.

Richard Burton graduated from the University of Leicester with a master's degree in mathematics. After graduating, he worked as a research engineer at the University of Leicester for a number of years, where he developed deep learning object detection models for their industrial partners. Now, he is working as a software engineer in the industry, where he continues to research the applications of deep learning in computer vision.

Nimesh Patel graduated from the University of Leicester with an MSc in applied computation and numerical modeling. During this time, a project collaboration with one of University of Leicester’s partners was undertaken, dealing with Machine Learning for Hand Gesture recognition. Since then, he has worked in the industry, researching Machine Learning for Computer Vision related tasks, such as Depth Estimation.

Leonardo Araujo is just the regular, Brazilian, curious engineer, who has worked in the industry for the past 19 years (yes, in Brazil, people work before graduation), doing HW/SW development and research on the topics of control engineering and computer vision. For the past 6 years, he has focused more on Machine Learning methods. His passions are too many to put on the book.

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