Practical Computer Vision Applications Using Deep Learning with CNNs: With Detailed Examples in Python Using TensorFlow and Kivy

This book teaches how to deploy deep learning applications into production across multiple platforms using the convolutional neural network (CNN) deep learning model and Python. It covers traditional machine-learning pipelines, artificial neural networks, and genetic algorithms, and demonstrates how CNNs are more efficient than fully connected ANNs. It also includes practical examples of image recognition models deployed to a web server using Flask and Kivy.

Format: Paperback / softback
Length: 405 pages
Publication date: 06 December 2018
Publisher: APress

Deploy Deep Learning Applications into Production Across Multiple Platforms

In this comprehensive guide, you will delve into the realm of deploying deep learning applications into production across diverse platforms. You will embark on a journey of computer vision applications that harness the power of the convolutional neural network (CNN) deep learning model, utilizing Python as your programming language.

The book begins by introducing the traditional machine-learning pipeline, where you will analyze an image dataset. Along the way, you will explore the fascinating world of artificial neural networks (ANNs), building one from scratch using Python, and then optimizing it through genetic algorithms.

Recognizing the limitations of traditional hand-crafted features in computer vision, the book highlights the remarkable advantages of the CNN deep-learning model as the state-of-art solution. It delves into the intricacies of CNNs, from their foundational concepts to their distinct and efficient architecture. You will implement a CNN in Python, providing a deep understanding of the model's workings.

Once you have consolidated the basics, you will leverage TensorFlow to construct a practical image-recognition model. This model will be deployed to a web server using Flask, making it accessible over the Internet. By incorporating Kivy and NumPy, you will create cross-platform data science applications with minimal overheads.

This book is designed to empower you with the knowledge and skills to apply deep learning and computer vision concepts from scratch, progressing step-by-step from conception to production. Whether you are a data scientist, machine learning engineer, software developer, or simply passionate about advancing the field of artificial intelligence, this book will be your invaluable companion.

By the end of this journey, you will have gained a profound understanding of how ANNs and CNNs work, developed computer vision applications and CNNs from scratch, followed a deep learning project from inception to production using TensorFlow, and utilized NumPy with Kivy to build cross-platform data science applications.

Unleash your potential and embark on a thrilling adventure into the world of deep learning and computer vision. Take the first step towards deploying your innovative solutions and making a lasting impact in the field.

Weight: 816g
Dimension: 181 x 254 x 25 (mm)
ISBN-13: 9781484241660
Edition number: 1st ed.