Описание: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.
This book addresses one of the most important unsolved problems in artificial intelligence: the task of learning, in an unsupervised manner, from massive quantities of spatiotemporal visual data that are available at low cost. The book covers important scientific discoveries and findings, with a focus on the latest advances in the field.
Presenting a coherent structure, the book logically connects novel mathematical formulations and efficient computational solutions for a range of unsupervised learning tasks, including visual feature matching, learning and classification, object discovery, and semantic segmentation in video. The final part of the book proposes a general strategy for visual learning over several generations of student-teacher neural networks, along with a unique view on the future of unsupervised learning in real-world contexts.
Offering a fresh approach to this difficult problem, several efficient, state-of-the-art unsupervised learning algorithms are reviewed in detail, complete with an analysis of their performance on various tasks, datasets, and experimental setups. By highlighting the interconnections between these methods, many seemingly diverse problems are elegantly brought together in a unified way.
Serving as an invaluable guide to the computational tools and algorithms required to tackle the exciting challenges in the field, this book is a must-read for graduate students seeking a greater understanding of unsupervised learning, as well as researchers in computer vision, machine learning, robotics, and related disciplines.
Описание: This book reviews the state of the art in deep learning approaches to high-performance robust disease detection, robust and accurate organ segmentation in medical image computing (radiological and pathological imaging modalities), and the construction and mining of large-scale radiology databases.
DO YOU WANT TO LEARN THE BASICS OF PYTHON PROGRAMMING QUICKLY?
Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it.
Some of the topics that we will discuss include:
The Fundamentals of Machine Learning, Deep learning, And Neural Networks
How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You
How To Master Neural Network Implementation Using Different Libraries
How Random Forest Algorithms Are Able To Help Out With Machine Learning
How To Uncover Hidden Patterns And Structures With Clustering
How Recurrent Neural Networks Work And When To Use
The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning
And Much More
This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like.
If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you.
Описание: Imagine a world where you can make a computer program learn for itself? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin?
Автор: Fingscheidt Название: Deep Neural Networks and Data for Automated Driving ISBN: 3031012321 ISBN-13(EAN): 9783031012327 Издательство: Springer Рейтинг: Цена: 6097.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges.
Описание: This book discusses several exciting research topics and applications in the intelligent Heterogenous Networks (Het-Net) and Internet of Things (IoT) era. The book presents the latest advances and research into heterogeneous networks in critical IoT applications.
Автор: Thomas Lindblad; Jason M. Kinser Название: Image Processing using Pulse-Coupled Neural Networks ISBN: 3642440339 ISBN-13(EAN): 9783642440335 Издательство: Springer Рейтинг: Цена: 15957.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews the neural theory and translates them into digital models. It gives applications in image recognition, foveation, image fusion and information extraction, and includes Python scripts that help readers replicate results presented in the text.
Автор: Thomas Lindblad; Jason M. Kinser Название: Image Processing using Pulse-Coupled Neural Networks ISBN: 364236876X ISBN-13(EAN): 9783642368769 Издательство: Springer Рейтинг: Цена: 18237.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book reviews the neural theory and translates them into digital models. It gives applications in image recognition, foveation, image fusion and information extraction, and includes Python scripts that help readers replicate results presented in the text.
Автор: Okechukwu A. Uwechue; Abhijit S. Pandya Название: Human Face Recognition Using Third-Order Synthetic Neural Networks ISBN: 1461368324 ISBN-13(EAN): 9781461368328 Издательство: Springer Рейтинг: Цена: 18294.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Human Face Recognition Using Third-Order Synthetic Neural Networks explores the viability of the application of High-order synthetic neural network technology to transformation-invariant recognition of complex visual patterns.
Автор: Ma, Yao (michigan State University) Tang, Jiliang (michigan State University) Название: Deep learning on graphs ISBN: 1108831745 ISBN-13(EAN): 9781108831741 Издательство: Cambridge Academ Рейтинг: Цена: 7126.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This comprehensive text on the theory and techniques of graph neural networks takes students, practitioners, and researchers from the basics to the state of the art. It systematically introduces foundational topics such as filtering pooling, robustness, and scalability and then demonstrates applications in NLP, data mining, vision and healthcare.
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