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This book presents revised reviewed versions of lectures given during the Machine Learning Summer School held in Canberra, Australia, in February 2002. The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning. Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.
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Dive into Deep Learning
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola
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R857
Discovery Miles 8 570
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Ships in 9 - 15 working days
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Deep learning has revolutionized pattern recognition, introducing
tools that power a wide range of technologies in such diverse
fields as computer vision, natural language processing, and
automatic speech recognition. Applying deep learning requires you
to simultaneously understand how to cast a problem, the basic
mathematics of modeling, the algorithms for fitting your models to
data, and the engineering techniques to implement it all. This book
is a comprehensive resource that makes deep learning approachable,
while still providing sufficient technical depth to enable
engineers, scientists, and students to use deep learning in their
own work. No previous background in machine learning or deep
learning is required—every concept is explained from scratch and
the appendix provides a refresher on the mathematics needed.
Runnable code is featured throughout, allowing you to develop your
own intuition by putting key ideas into practice.
State-of-the-art algorithms and theory in a novel domain of machine
learning, prediction when the output has structure. Machine
learning develops intelligent computer systems that are able to
generalize from previously seen examples. A new domain of machine
learning, in which the prediction must satisfy the additional
constraints found in structured data, poses one of machine
learning's greatest challenges: learning functional dependencies
between arbitrary input and output domains. This volume presents
and analyzes the state of the art in machine learning algorithms
and theory in this novel field. The contributors discuss
applications as diverse as machine translation, document markup,
computational biology, and information extraction, among others,
providing a timely overview of an exciting field. Contributors
Yasemin Altun, Goekhan Bakir, Olivier Bousquet, Sumit Chopra,
Corinna Cortes, Hal Daume III, Ofer Dekel, Zoubin Ghahramani, Raia
Hadsell, Thomas Hofmann, Fu Jie Huang, Yann LeCun, Tobias Mann,
Daniel Marcu, David McAllester, Mehryar Mohri, William Stafford
Noble, Fernando Perez-Cruz, Massimiliano Pontil, Marc'Aurelio
Ranzato, Juho Rousu, Craig Saunders, Bernhard Schoelkopf, Matthias
W. Seeger, Shai Shalev-Shwartz, John Shawe-Taylor, Yoram Singer,
Alexander J. Smola, Sandor Szedmak, Ben Taskar, Ioannis
Tsochantaridis, S.V.N Vishwanathan, Jason Weston
A comprehensive introduction to Support Vector Machines and related
kernel methods. In the 1990s, a new type of learning algorithm was
developed, based on results from statistical learning theory: the
Support Vector Machine (SVM). This gave rise to a new class of
theoretically elegant learning machines that use a central concept
of SVMs--kernels-for a number of learning tasks. Kernel machines
provide a modular framework that can be adapted to different tasks
and domains by the choice of the kernel function and the base
algorithm. They are replacing neural networks in a variety of
fields, including engineering, information retrieval, and
bioinformatics. Learning with Kernels provides an introduction to
SVMs and related kernel methods. Although the book begins with the
basics, it also includes the latest research. It provides all of
the concepts necessary to enable a reader equipped with some basic
mathematical knowledge to enter the world of machine learning using
theoretically well-founded yet easy-to-use kernel algorithms and to
understand and apply the powerful algorithms that have been
developed over the last few years.
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