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 of Deep Neural Network and Convolutional Neural Network
• Milestones (some famous networks)
• Deep Convolutional Neural Network
• Conclusion

Presentation Slides

References

[1] He, Kaiming, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. “Deep Residual Learning for Image Recognition.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, , 770–78.

[2] Hinton, Geoffrey E, and Ruslan R Salakhutdinov. 2006. “Reducing the Dimensionality of Data with Neural Networks.” science 313(5786): 504–7.

[3] Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E Hinton. 2012. “Imagenet Classification with Deep Convolutional Neural Networks.” In Advances in Neural Information Processing Systems, , 1097–1105.

[4] LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. 1998. “Gradient-Based Learning Applied to Document Recognition.” Proceedings of the IEEE 86(11): 2278–2324.

[5] Simonyan, Karen, and Andrew Zisserman. 2014. “Very Deep Convolutional Networks for Large-Scale Image Recognition.” arXiv preprint arXiv:1409.1556.

[6] Andrew, Ng, Kian Katanforoosh, and Younes Bensouda Mourri. 2019. “Deep Learning.” Coursera. https://www.coursera.org/specializations/deep-learning (February 23, 2019).

[7]     W.C. Siu, Z.S. Liu, J.J. Huang and K.W. Hung, "Learning Approaches for Super-Resolution Imaging", Chapter 8 of  "Learning Approaches in Signal Processing", Pan Stanford Series on Digital Signal Processing, Vol.2, November 2018 (Edited by W.C. Siu, L.P. Chau, L. Wang and T. Tan), November 2018.