000			03770cam a2200301zu 4500
001			88856744
003			FRCYB88856744
005			20250107230222.0
006			m o d
007			cr un
008			250108s2018 fr | o|||||0|0|||eng d
020			_a9781788392303
035			_aFRCYB88856744
040			_aFR-PaCSA _ben _c _erda
100	1		_aSewak, Mohit
245	0	1	_aPractical Convolutional Neural Networks _c['Sewak, Mohit', 'Karim, Md. Rezaul', 'Pujari, Pradeep']
264		1	_bPackt Publishing _c2018
300			_a p.
336			_btxt _2rdacontent
337			_bc _2rdamdedia
338			_bc _2rdacarrier
650		0	_a
700	0		_aSewak, Mohit
700	0		_aKarim, Md. Rezaul
700	0		_aPujari, Pradeep
856	4	0	_2Cyberlibris _uhttps://international.scholarvox.com/netsen/book/88856744 _qtext/html _a
520			_aOne stop guide to implementing award-winning, and cutting-edge CNN architecturesAbout This BookFast-paced guide with use cases and real-world examples to get well versed with CNN techniquesImplement CNN models on image classification, transfer learning, Object Detection, Instance Segmentation, GANs and moreImplement powerful use-cases like image captioning, reinforcement learning for hard attention, and recurrent attention modelsWho 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.What You Will LearnFrom CNN basic building blocks to advanced concepts understand practical areas they can be applied toBuild an image classifier CNN model to understand how different components interact with each other, and then learn how to optimize itLearn different algorithms that can be applied to Object Detection, and Instance Segmentation Learn advanced concepts like attention mechanisms for CNN to improve prediction accuracyUnderstand transfer learning and implement award-winning CNN architectures like AlexNet, VGG, GoogLeNet, ResNet and moreUnderstand the working of generative adversarial networks and how it can create new, unseen imagesIn DetailConvolutional 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.Style and approachAn easy to follow concise and illustrative guide explaining the core concepts of ConvNets to help you understand, implement and deploy your CNN models quickly.
999			_c70367 _d70367