1. Networks: Neural Networks
Ali Cole
Madison Kutchey
Charly Mccown
Xavier henes

2. Definition
A directed network based on the structure of connections within an organism's brain
Many inputs and only a couple outputs
Excitatory or inhibiting
Combination allows for complex information processing tasks
Artificial neural networks
Handout: Image of a neuron structure (Figure 5.6 in Newman, pg 95, or similar)

3. Network Characteristics
Nodes and Edges
Value of Inputs
(Puri, Chapter 1)
Neuron Types
(Newman, Section 2.5)
Multiple Layers
("The Basics …")
Machine Learning 
Feed Forward and Back Propagation

4. Mathematical Methods Delta rule Epoch method
Gradient descent—mountian climber analogy
Updates weights of neurons
Special case of backpropagation method
Epoch method
Used for training neural networks
Epoch = when algorithm goes over entire data set
No way to determine perfect number of epochs for any given network
Special case of backpropagation 

5. Research questions Applications Medicine Data Science Business/Finance
Estimating Breast Cancer Risks Using Neural Networks
Data Science
Cloud Service for Data Analysis in Medical Information Systems Using Artificial Neural Networks
Business/Finance
Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity

6. Works Cited
“Epoch vs Batch Size vs Iterations – Towards Data Science.”Towards Data Science, Towards Data Science, 23 Sept. 2017, towardsdatascience.com/epoch-vs- iterations-vs-batch-size-4dfb9c7ce9c9.
“Delta Rule.” Wikipedia, Wikimedia Foundation, 8 Jan. 2018, en.wikipedia.org/wiki/Delta_rule.
Ellacott, S.W. “Techniques for the Mathematical Analysis of Neural Networks.” Journal of Computational and Applied Mathematics, vol. 50, no. 1-3, 1994, pp. 283–297., doi: / (94)
“Epoch vs Iteration When Training Neural Networks.” Stack Overflow, 27 May 2017, stackoverflow.com/questions/ /epoch-vs-iteration-when-training-neural-networks.
“How Neural Networks Are Trained.” Github, ml4a.github.io/ml4a/how_neural_networks_are_trained/.
Networks, Crowds and Markets, Reasoning about a Highly Connected World, David Easley, Jon Kleinberg, Cambridge University Press, ISBN:
Networks, An Introduction, M.E.J. Newman, Oxford University Press, ISMN:
Puri, Munish. Artificial Neural Network for Drug Design, Delivery and Disposition. Academic Press Is an Imprint of Elsevier, 2016.
Ristolainen, Kim. “Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity.” The Scandinavian Journal of Economics, vol. 120, no. 1, 28 Dec , pp. 31–62., doi: /sjoe
“The Basics of Neural Networks.” A Basic Introduction To Neural Networks, pages.cs.wisc.edu/~bolo/shipyard/neural/local.html.