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2514 results in Pattern Recognition and Machine Learning

Page 45 of 126

  • 1 - Pretraining

  • Daniel A. Roberts, Massachusetts Institute of Technology, Sho Yaida
  • With contributions by Boris Hanin
  • Book:
    The Principles of Deep Learning Theory
    Published online:
    05 May 2022
    Print publication:
    26 May 2022, pp 11-36
    • Chapter
      • You have access
    • PDF
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  • 0 - Initialization

  • Daniel A. Roberts, Massachusetts Institute of Technology, Sho Yaida
  • With contributions by Boris Hanin
  • Book:
    The Principles of Deep Learning Theory
    Published online:
    05 May 2022
    Print publication:
    26 May 2022, pp 1-10
    • Chapter
      • You have access
    • PDF
    • Export citation

Control Systems and Reinforcement Learning
  • Sean Meyn
  • Published online:
    17 May 2022
    Print publication:
    09 June 2022
  • A high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of 'deep' or 'Q', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.

Introduction to Graph Signal Processing
  • Antonio Ortega
  • Published online:
    09 May 2022
    Print publication:
    09 June 2022
  • An intuitive and accessible text explaining the fundamentals and applications of graph signal processing. Requiring only an elementary understanding of linear algebra, it covers both basic and advanced topics, including node domain processing, graph signal frequency, sampling, and graph signal representations, as well as how to choose a graph. Understand the basic insights behind key concepts and learn how graphs can be associated to a range of specific applications across physical, biological and social networks, distributed sensor networks, image and video processing, and machine learning. With numerous exercises and Matlab examples to help put knowledge into practice, and a solutions manual available online for instructors, this unique text is essential reading for graduate and senior undergraduate students taking courses on graph signal processing, signal processing, information processing, and data analysis, as well as researchers and industry professionals.

Page 45 of 126