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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

  • Hopfield, John J, Neural networks and physical systems with emergent collective computational abilities. Proceedings of the national academy of sciences 79.8 (1982): 2554-2558.
  • Bengio, Yoshua, Patrice Simard, and Paolo Frasconi, Learning long-term dependencies with gradient descent is difficult. IEEE transactions on neural networks 5.2 (1994): 157-166.
  • Hochreiter, Sepp, and Jürgen Schmidhuber, long short-term memory. Neural Computation 9.8 (1997): 1735-1780.
  • Hochreiter, Sepp. Recurrent neural net learning and vanishing gradient. International Journal Of Uncertainity, Fuzziness and Knowledge-Based Systems 6.2 (1998): 107-116.
  • Sutskever, Ilya, Training recurrent neural networks. University of Toronto, Toronto, Ont., Canada (2013).
  • Chung, Junyoung, et al, Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555...
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