Fundamentals of Complex Analysis (3版)

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書名：Fundamentals of Complex Analysis (PNIE) 3/e 作者：Saff 出版社：Pearson 出版日期：2014/00/00 ISBN：9781292023755 目錄 1. Complex Numbers. 2. Analytic Functions. 3. Elementary Functions. 4. Complex Integration. 5. Series Representations for Analytic Functions. 6. Residue Theory. 7. Conformal Mapping. 8. The Transforms of Applied Mathematics Answers to Odd-Numbered Problems.

First Aid Clinical Algorithms for the USMLE Step 2 CK

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【簡介】 重量：1.84kg 頁數：872 裝訂：平裝　開數：25.2*18.2 cm 印刷:彩色 First Aid™ remains the most trusted name in USMLE® review―just ask any medical student! First Aid Clinical Algorithms for the USMLE Step 2 CK was designed to help you develop and refine your clinical thinking skills―preparing you for success in your clerkship and on the Step 2 CK exam. This new addition to the First Aid™ series uses an algorithmic approach that allows you to methodically walk through the diagnostic decision making process. The content is organized by symptom to reflect how information will be presented in an actual clinical setting. Clinical vignettes focusing on specific illnesses are followed by step-by-step flowcharts outlining the diagnosis and treatment of the illness and its relevant differential diagnoses. Unique algorithmic approach Clinical vignettes accompany decision-making algorithms Flowcharts outline the diagnosis and treatment process

MACHINE LEARNING ALGORITHMS FOR SIGNAL AND IMAGE PROCESSING (1版)

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DESCRIPTION Machine Learning Algorithms for Signal and Image Processing Enables readers to understand the fundamental concepts of machine and deep learning techniques with interactive, real-life applications within signal and image processing Machine Learning Algorithms for Signal and Image Processing aids the reader in designing and developing real-world applications using advances in machine learning to aid and enhance speech signal processing, image processing, computer vision, biomedical signal processing, adaptive filtering, and text processing. It includes signal processing techniques applied for pre-processing, feature extraction, source separation, or data decompositions to achieve machine learning tasks. Written by well-qualified authors and contributed to by a team of experts within the field, the work covers a wide range of important topics, such as: Speech recognition, image reconstruction, object classification and detection, and text processing Healthcare monitoring, biomedical systems, and green energy How various machine and deep learning techniques can improve accuracy, precision rate recall rate, and processing time Real applications and examples, including smart sign language recognition, fake news detection in social media, structural damage prediction, and epileptic seizure detection Professionals within the field of signal and image processing seeking to adapt their work further will find immense value in this easy-to-understand yet extremely comprehensive reference work. It is also a worthy resource for students and researchers in related fields who are looking to thoroughly understand the historical and recent developments that have been made in the field.

Algorithms for Convex Optimization

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In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself. A one-stop guide to essential algorithms and methods for a wide computer science audience 166 guided exercises cover all major algorithms

Algorithms for Convex Optimization

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In the last few years, Algorithms for Convex Optimization have revolutionized algorithm design, both for discrete and continuous optimization problems. For problems like maximum flow, maximum matching, and submodular function minimization, the fastest algorithms involve essential methods such as gradient descent, mirror descent, interior point methods, and ellipsoid methods. The goal of this self-contained book is to enable researchers and professionals in computer science, data science, and machine learning to gain an in-depth understanding of these algorithms. The text emphasizes how to derive key algorithms for convex optimization from first principles and how to establish precise running time bounds. This modern text explains the success of these algorithms in problems of discrete optimization, as well as how these methods have significantly pushed the state of the art of convex optimization itself. A one-stop guide to essential algorithms and methods for a wide computer science audience 166 guided exercises cover all major algorithms