← Introduction to Machine Learning
Neural Networks
Resources
Books
- Russell, S. and Norvig, P. (2010). Artificial Intelligence: A Modern Approach 3rd ed - Chapter 19 . Prentice Hall
- Bishop, C. (2006). Pattern Recognition and Machine Learning. Springer - Chapter 5
- Deisenroth M. P. et. al. (2020). Mathematics for Machine Learning - Chapter 10
- Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press - Chapters 6-9
- LeCun, Y., Bengio, Y., Hinton, G. (2015). Deep learning. Nature, 521(7553), 436-444
- Nielsen, M. (2019). Neural Networks and Deep Learning
Papers and Reports
- Rumelhart, D. E., Hinton, G. E., Williams, R. J. (1986). Learning representations by back-propagating errors
- Cybenko, G. (1989). Approximation by superpositions of a sigmoidal function
- Hornik, K., Stinchcombe, M., White, H. (1989). Multilayer feedforward networks are universal approximators
- LeCun, Y., et al. (1998). Gradient-based learning applied to document recognition
- Krizhevsky, A., Sutskever, I., Hinton, G. E. (2012). ImageNet classification with deep convolutional neural networks
- He, K., et al. (2016). Deep residual learning for image recognition
- Kingma, D. P., Ba, J. (2014). Adam: A method for stochastic optimization
- Srivastava, N., et al. (2014). Dropout: a simple way to prevent neural networks from overfitting
- Glorot, X., Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks
- He, K., et al. (2015). Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification