Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, medical discoveries, innovative eCommerce and more.You will learn to create innovative solutions around image and video analytics to solve complex machine learning and computer vision related problems and implement real-life CNN models.
All of the code is organized into folders. Each folder starts with a number followed by the application name. For example, Chapter02.
The code will look like the following:
import tensorflow as tf #Creating TensorFlow object hello_constant = tf.constant('Hello World!', name = 'hello_constant') #Creating a session object for execution of the computational graph with tf.Session() as sess:
This book is focused on building CNNs with Python programming language. We have used Python version 2.7 (2x) to build various applications and the open source and enterprise-ready professional software using Python, Spyder, Anaconda, and PyCharm. Many of the examples are also compatible with Python 3x. As a good practice, we encourage users to use Python virtual environments for implementing these codes.
We focus on how to utilize various Python and deep learning libraries (Keras, TensorFlow, and Caffe) in the best possible way to build real-world applications. In that spirit, we have tried to keep all of the code as friendly and readable as possible. We feel that this will enable our readers to easily understand the code and readily use it in different scenarios.
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