R Deep Learning Essentials - Second Edition

References/further reading

These papers are classical deep learning papers in this domain. Some of them document winning approaches to ImageNet competitions. I encourage you to download and read all of them. You may not understand them at first, but their importance will become more evident as you continue on your journey in deep learning.

• Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. Advances in neural information processing systems. 2012.
• Szegedy, Christian, et al. Going Deeper with Convolutions. Cvpr, 2015.
• LeCun, Yann, et al. Learning Algorithms for Classification: A Comparison on Handwritten Digit Recognition. Neural networks: the statistical mechanics perspective ...