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Deep Learning for Beginners

You're reading from  Deep Learning for Beginners

Product type Book
Published in Sep 2020
Publisher Packt
ISBN-13 9781838640859
Pages 432 pages
Edition 1st Edition
Languages
Author (1):
Dr. Pablo Rivas Dr. Pablo Rivas
Profile icon Dr. Pablo Rivas

Table of Contents (20) Chapters

Preface Section 1: Getting Up to Speed
Introduction to Machine Learning Setup and Introduction to Deep Learning Frameworks Preparing Data Learning from Data Training a Single Neuron Training Multiple Layers of Neurons Section 2: Unsupervised Deep Learning
Autoencoders Deep Autoencoders Variational Autoencoders Restricted Boltzmann Machines Section 3: Supervised Deep Learning
Deep and Wide Neural Networks Convolutional Neural Networks Recurrent Neural Networks Generative Adversarial Networks Final Remarks on the Future of Deep Learning Other Books You May Enjoy

References

  • Cleveland Heart Disease Dataset (1988). Principal investigators:
    a. Hungarian Institute of Cardiology. Budapest: Andras Janosi, M.D.
    b. University Hospital, Zurich, Switzerland: William Steinbrunn, M.D.
    c. University Hospital, Basel, Switzerland: Matthias Pfisterer, M.D.
    d. V.A. Medical Center, Long Beach and Cleveland Clinic Foundation: Robert Detrano, M.D., Ph.D.
  • Detrano, R., Janosi, A., Steinbrunn, W., Pfisterer, M., Schmid, J.J., Sandhu, S., Guppy, K.H., Lee, S. and Froelicher, V., (1989). International application of a new probability algorithm for the diagnosis of coronary artery disease. The American journal of cardiology, 64(5), 304-310.
  • Deng, L. (2012). The MNIST database of handwritten digit images for machine learning research (best of the web). IEEE Signal Processing Magazine, 29(6), 141-142.
  • Sezgin, M., and Sankur, B. (2004). Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic imaging, 13(1), 146-166.
  • Potdar...
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