BOOKS AND REFERENCES

Deep learning is a rapidly evolving field, and we will hence use multiple sources of references, including books, blogs and articles, each of which will be pointed out at the end of each topic.

References for deep learning:

Ian Goodfellow, Yoshua Bengio, Aaron Courville, Deep Learning, 2016

Michael Nielsen, Neural Networks and Deep Learning, 2016

Yoshua Bengio, Learning Deep Architectures for AI, 2009

References for computer vision:

Richard Szeliski, Computer Vision: Algorithms and Applications, 2010.

Simon Prince, Computer Vision: Models, Learning, and Inference, 2012.

David Forsyth, Jean Ponce, Computer Vision: A Modern Approach, 2002.

Tools: We will use PyTorch for our assignments.

Other useful references:

Bishop, Christopher. Neural Networks for Pattern Recognition. New York, NY: Oxford University Press, 1995. ISBN: 9780198538646.
Bishop, Christopher M. Pattern Recognition and Machine Learning. Springer, 2006. ISBN 978-0-387-31073-2
Duda, Richard, Peter Hart, and David Stork. Pattern Classification. 2nd ed. New York, NY: Wiley-Interscience, 2000. ISBN: 9780471056690.
Mitchell, Tom. Machine Learning. New York, NY: McGraw-Hill, 1997. ISBN: 9780070428072.
Richard Hartley, Andrew Zisserman, Multiple View Geometry in Computer Vision, 2004.
David Marr, Vision, 1982.

NOC: Deep Learning for Computer Vision, IIT Hyderabad