1 |
G. Cybenko, Approximations by superpositions of sigmoidal functions, Mathematics of Control, Signals, and Systems 2 (4) (1989), 303-314.
DOI
|
2 |
M.T. Hagan, M.H Beale, H.B. Demuth and O.D Jesus, Neural network Design, 2nd Ed.
|
3 |
M.T. Hagan, Neural network design, free book from http://hagan.okstate.edu/NNDesign.pdf
|
4 |
J. J. Hopfield, Neural networks and physical systems with emergent collective computational abilities, Proceedings of the National Academy of Sciences 79 (1982), 2554-2558.
DOI
|
5 |
K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks 4 (2) (1991), 251-257.
DOI
|
6 |
Bitna Kim, Handwritten digits classication by neural networks with small data, Master's thesis, Kangwon National University, 2018.
|
7 |
P. Kim, Matlab deep learning, Apress, 2017
|
8 |
T. Kohonen, Correlation matrix memories,IEEE Transactions on Computers 21 (1972), 353-359.
|
9 |
W.S. McCulloch and W.H. Pitts, A logical calculus of the ideas immanent in nervous activity, Bull. Math. Biophysics 5 (1943) 115-133.
DOI
|
10 |
M. Minsky and S. Papert, Perceptrons: an introduction to computational geometry, M.I.T. Press, Cambridge, 1969
|
11 |
MNIST, http://yann.lecun.com/exdb/mnist/
|
12 |
A. Ng, Course on machine learing, Cousera, https://www.coursera.org/learn/machine-learning
|
13 |
A.Ng, CS229 lecture notes, http://cs229.standford.edu
|
14 |
M. Nielsen, Neural networks and deep learning, http://neuralnetworksanddeeplearning.com
|
15 |
UFLDL, Using the MNIST dataset, http://udl.stanford.edu/wiki/index.php/Using_the_MNIST_Dataset
|
16 |
T. Rashid, Make your own neural network, CreatSpace, 2016
|
17 |
F. Rosenblatt, The perceptron: A probabilistic model for information storage and organization in the brain, Psycho-logical Review 65 (1958), 386-408.
DOI
|
18 |
D. E. Rumelhart and J. L. McClelland, eds., Parallel Distributed Processing: Explorations, Microstructure of Cognition, Vol. 1, Cambridge, MA: MIT Press, 1986.
|
19 |
J.R. Shewchuk, An introduction to the conjugate gradient method eithout the agonizing pain, Technical report, Carnegie Mellon University, 1994
|
20 |
Mathworks, MATLAB documentation, MATLAB version R2016a, 2016
|
21 |
UFLDL tutorial, Unsupervised feature learning and deep learning, http://deeplearning.stanford.edu/wiki/index.php/UFLDLTutorial
|
22 |
http://udl.stanford.edu/wiki/resources/mnistHelper.zip
|
23 |
Wikipedia, https://en.wikipedia.org/
|