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Books > Computing & IT > Applications of computing > Artificial intelligence > Neural networks
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Practical Convolutional Neural Networks - Implement advanced deep learning models using Python (Paperback)
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Practical Convolutional Neural Networks - Implement advanced deep learning models using Python (Paperback)
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One stop guide to implementing award-winning, and cutting-edge CNN
architectures Key Features Fast-paced guide with use cases and
real-world examples to get well versed with CNN techniques
Implement CNN models on image classification, transfer learning,
Object Detection, Instance Segmentation, GANs and more Implement
powerful use-cases like image captioning, reinforcement learning
for hard attention, and recurrent attention models Book
DescriptionConvolutional 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. This
book starts with an overview of deep neural networkswith the
example of image classification and walks you through building your
first CNN for human face detector. We will learn to use concepts
like transfer learning with CNN, and Auto-Encoders to build very
powerful models, even when not much of supervised training data of
labeled images is available. Later we build upon the learning
achieved to build advanced vision related algorithms for object
detection, instance segmentation, generative adversarial networks,
image captioning, attention mechanisms for vision, and recurrent
models for vision. By the end of this book, you should be ready to
implement advanced, effective and efficient CNN models at your
professional project or personal initiatives by working on complex
image and video datasets. What you will learn From CNN basic
building blocks to advanced concepts understand practical areas
they can be applied to Build an image classifier CNN model to
understand how different components interact with each other, and
then learn how to optimize it Learn different algorithms that can
be applied to Object Detection, and Instance Segmentation Learn
advanced concepts like attention mechanisms for CNN to improve
prediction accuracy Understand transfer learning and implement
award-winning CNN architectures like AlexNet, VGG, GoogLeNet,
ResNet and more Understand the working of generative adversarial
networks and how it can create new, unseen images Who this book is
forThis book is for data scientists, machine learning and deep
learning practitioners, Cognitive and Artificial Intelligence
enthusiasts who want to move one step further in building
Convolutional Neural Networks. Get hands-on experience with extreme
datasets and different CNN architectures to build efficient and
smart ConvNet models. Basic knowledge of deep learning concepts and
Python programming language is expected.
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