Conclude AI chapter

assets/bibliography.bib

@@ -561,7 +561,7 @@
   year            = 2010,
   series          = {Prentice Hall Series in Artificial Intelligence},
   edition         = {3rd},
-  pages           = {38-45, 55-56}
+  pages           = {38-45, 48-49, 55-56}
 }
 
 @article{McCarthy_Minsky_Rochester_Shannon_2006,
@@ -599,3 +599,66 @@
   edition         = 2,
   pages           = {39-40}
 }
+
+@Inbook{Zou2009,
+  author          = "Zou, Jinming and Han, Yi and So, Sung-Sau",
+  editor          = "Livingstone, David J.",
+  title           = "Overview of Artificial Neural Networks",
+  bookTitle       = "Artificial Neural Networks: Methods and Applications",
+  year            = 2009,
+  publisher       = "Humana Press",
+  address         = "Totowa, NJ",
+  pages           = "14--22",
+  abstract        = "The artificial neural network (ANN), or simply neural
+                  network, is a machine learning method evolved from the idea of
+                  simulating the human brain. The data explosion in modern drug
+                  discovery research requires sophisticated analysis methods to
+                  uncover the hidden causal relationships between single or
+                  multiple responses and a large set of properties. The ANN is
+                  one of many versatile tools to meet the demand in drug
+                  discovery modeling. Compared to a traditional regression
+                  approach, the ANN is capable of modeling complex nonlinear
+                  relationships. The ANN also has excellent fault tolerance and
+                  is fast and highly scalable with parallel processing. This
+                  chapter introduces the background of ANN development and
+                  outlines the basic concepts crucially important for
+                  understanding more sophisticated ANN. Several commonly used
+                  learning methods and network setups are discussed briefly at
+                  the end of the chapter.",
+  isbn            = "978-1-60327-101-1",
+  doi             = "10.1007/978-1-60327-101-1_2",
+  url             = "https://doi.org/10.1007/978-1-60327-101-1_2"
+}
+
+@book{book:2610592,
+  title           = {Principles of artificial neural networks},
+  author          = {Graupe, Daniel},
+  publisher       = {World Scientific Publ},
+  isbn            = {9789814522731,9814522732},
+  year            = 2013,
+  edition         = {3. ed},
+  pages           = {28-31}
+}
+
+@Article{Cireşan2010,
+  author          = {Cire{\c{s}}an, Dan Claudiu and Meier, Ueli and Gambardella,
+                  Luca Maria and Schmidhuber, J{\"u}rgen},
+  title           = {Deep, Big, Simple Neural Nets for Handwritten Digit
+                  Recognition},
+  journal         = {Neural Computation},
+  year            = 2010,
+  month           = {Dec},
+  day             = 01,
+  volume          = 22,
+  number          = 12,
+  pages           = {3207-3220},
+  abstract        = {Good old online backpropagation for plain multilayer
+                  perceptrons yields a very low 0.35{\%} error rate on the MNIST
+                  handwritten digits benchmark. All we need to achieve this best
+                  result so far are many hidden layers, many neurons per layer,
+                  numerous deformed training images to avoid overfitting, and
+                  graphics cards to greatly speed up learning.},
+  issn            = {0899-7667},
+  doi             = {10.1162/NECO_a_00052},
+  url             = {https://doi.org/10.1162/NECO_a_00052}
+}