9 - Deep Learning - Plain Version 2020/ClipID:21051 previous clip next clip

The automatic subtitles generated using Whisper Open AI in this video player (and in the Multistream video player) are provided for convenience and accessibility purposes. However, please note that accuracy and interpretation may vary. For more information, please refer to the FAQs (Paragraph 14).
Recording date 2020-10-10

Via

Free

Language

English

Organisational Unit

Lehrstuhl für Informatik 5 (Mustererkennung)

Producer

Lehrstuhl für Informatik 5 (Mustererkennung)

Format

lecture

Deep Learning - Feedforward Networks Part 4

This video explains backpropagation at the level of layer abstraction.

For reminders to watch the new video follow on Twitter or LinkedIn.

Further Reading:
A gentle Introduction to Deep Learning 

References
[1] R. O. Duda, P. E. Hart, and D. G. Stork. Pattern Classification. John Wiley and Sons, inc., 2000.
[2] Christopher M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006.
[3] F. Rosenblatt. “The perceptron: A probabilistic model for information storage and organization in the brain.” In: Psychological Review 65.6 (1958), pp. 386–408.
[4] WS. McCulloch and W. Pitts. “A logical calculus of the ideas immanent in nervous activity.” In: Bulletin of mathematical biophysics 5 (1943), pp. 99–115.
[5] D. E. Rumelhart, G. E. Hinton, and R. J. Williams. “Learning representations by back-propagating errors.” In: Nature 323 (1986), pp. 533–536.
[6] Xavier Glorot, Antoine Bordes, and Yoshua Bengio. “Deep Sparse Rectifier Neural Networks”. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence Vol. 15. 2011, pp. 315–323.
[7] William H. Press, Saul A. Teukolsky, William T. Vetterling, et al. Numerical Recipes 3rd Edition: The Art of Scientific Computing. 3rd ed. New York, NY, USA: Cambridge University Press, 2007.

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