## Objectives

Deep learning is a recently hot machine learning method. The deep learning architectures are formed by the composition of several nonlinear transformations with the goal to yield more abstract and extract useful representations/features. (i) Start with a revision of the basic principle of Neural Networks, neutron structure, examples of back-propagation, learning procedure and iterations (preferably with experimental results), and then (ii) discuss at least one type of deep learning architecture, with a way or ways to illustrate its working principle. (iii) You can also give a summary of different deep learning architectures, and highlight their uses and significances with a good explanation if possible. (iv) you can also illustrate the whole procedure for its use *for object recognition/classification*.

## Outlines

- Introduction
- Effect of a Neuron
- Examples of Binary Classification
- Conclusion

##

Presentation Slides

Deep-Learning-1-NN_v15## Q&A

NN_V2## Recommended References

[1] S. Ruder, “An overview of gradient descent
optimization algorithms,” *arXiv Prepr. arXiv1609.04747*, 2016.

[2] L.
Bottou, “Large-scale machine learning with stochastic gradient descent,” in *Proceedings
of COMPSTAT’2010*, Springer, 2010, pp. 177–186.

[3] N. Andrew, K. Katanforoosh, and Y. B. Mourri, “Deep Learning,” *Coursera*, 2019. [Online]. Available: https://www.coursera.org/specializations/deep-learning. [Accessed: 23-Feb-2019].

[4] W.C. Siu, Z.S. Liu, J.J. Huang