Signal Processing and Machine Learning Theory
eBook - ePub

Signal Processing and Machine Learning Theory

  1. 1,234 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Signal Processing and Machine Learning Theory

About this book

Signal Processing and Machine Learning Theory, authored by world-leading experts, reviews the principles, methods and techniques of essential and advanced signal processing theory. These theories and tools are the driving engines of many current and emerging research topics and technologies, such as machine learning, autonomous vehicles, the internet of things, future wireless communications, medical imaging, etc. - Provides quick tutorial reviews of important and emerging topics of research in signal processing-based tools - Presents core principles in signal processing theory and shows their applications - Discusses some emerging signal processing tools applied in machine learning methods - References content on core principles, technologies, algorithms and applications - Includes references to journal articles and other literature on which to build further, more specific, and detailed knowledge

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Yes, you can access Signal Processing and Machine Learning Theory by Paulo S.R. Diniz in PDF and/or ePUB format, as well as other popular books in Technology & Engineering & Signals & Signal Processing. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Signal Processing and Machine Learning Theory
  2. Chapter 1 Introduction to signal processing and machine learning theory
  3. Chapter 2 Continuous-time signals and systems
  4. Chapter 3 Discrete-time signals and systems
  5. Chapter 4 Random signals and stochastic processes
  6. Chapter 5 Sampling and quantization
  7. Chapter 6 Digital filter structures and their implementation
  8. Chapter 7 Multirate signal processing for software radio architectures
  9. Chapter 8 Modern transform design for practical audio/image/video coding applications
  10. Chapter 9 Data representation: from multiscale transforms to neural networks
  11. Chapter 10 Frames in signal processing
  12. Chapter 11 Parametric estimation
  13. Chapter 12 Adaptive filters
  14. Chapter 13 Machine learning
  15. Chapter 14 A primer on graph signal processing
  16. Chapter 15 Tensor methods in deep learning
  17. Chapter 16 Nonconvex graph learning: sparsity, heavy tails, and clustering
  18. Chapter 17 Dictionaries in machine learning
  19. Index