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Machine Learning for Developers

You're reading from   Machine Learning for Developers Uplift your regular applications with the power of statistics, analytics, and machine learning

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Product type Paperback
Published in Oct 2017
Publisher Packt
ISBN-13 9781786469878
Length 270 pages
Edition 1st Edition
Languages
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Authors (2):
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Md Mahmudul Hasan Md Mahmudul Hasan
Author Profile Icon Md Mahmudul Hasan
Md Mahmudul Hasan
Rodolfo Bonnin Rodolfo Bonnin
Author Profile Icon Rodolfo Bonnin
Rodolfo Bonnin
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introduction - Machine Learning and Statistical Science 2. The Learning Process FREE CHAPTER 3. Clustering 4. Linear and Logistic Regression 5. Neural Networks 6. Convolutional Neural Networks 7. Recurrent Neural Networks 8. Recent Models and Developments 9. Software Installation and Configuration

References

  • Fukushima, Kunihiko, and Sei Miyake, Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition. Competition and cooperation in neural nets. Springer, Berlin, Heidelberg, 1982. 267-285.
  • LeCun, Yann, et al. Gradient-based learning applied to document recognition. Proceedings of the IEEE 86.11 (1998): 2278-2324.
  • Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. 2012.
  • Hinton, Geoffrey E., et al, Improving Neural Networks by Preventing Co-Adaptation of Feature Detectors. arXiv preprint arXiv:1207.0580 (2012).
  • Simonyan, Karen, and Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556 (2014).
  • Srivastava, Nitish, et al. Dropout: A Simple Way to Prevent...
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