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Practical Computer Vision Applications Using Deep Learning with CNNs - With Detailed Examples in Python Using TensorFlow and Kivy (Paperback, 1st ed.)
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Practical Computer Vision Applications Using Deep Learning with CNNs - With Detailed Examples in Python Using TensorFlow and Kivy (Paperback, 1st ed.)
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Deploy deep learning applications into production across multiple
platforms. You will work on computer vision applications that use
the convolutional neural network (CNN) deep learning model and
Python. This book starts by explaining the traditional
machine-learning pipeline, where you will analyze an image dataset.
Along the way you will cover artificial neural networks (ANNs),
building one from scratch in Python, before optimizing it using
genetic algorithms. For automating the process, the book highlights
the limitations of traditional hand-crafted features for computer
vision and why the CNN deep-learning model is the state-of-art
solution. CNNs are discussed from scratch to demonstrate how they
are different and more efficient than the fully connected ANN
(FCNN). You will implement a CNN in Python to give you a full
understanding of the model. After consolidating the basics, you
will use TensorFlow to build a practical image-recognition model
that you will deploy to a web server using Flask, making it
accessible over the Internet. Using Kivy and NumPy, you will create
cross-platform data science applications with low overheads. This
book will help you apply deep learning and computer vision concepts
from scratch, step-by-step from conception to production. What You
Will Learn Understand how ANNs and CNNs work Create computer vision
applications and CNNs from scratch using Python Follow a deep
learning project from conception to production using TensorFlow Use
NumPy with Kivy to build cross-platform data science applications
Who This Book Is ForData scientists, machine learning and deep
learning engineers, software developers.
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