# Big Data - Exam II

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1. neural computing
a pattern-recognition methodology for machine learning with the ability to "learn" from data, and the ability to generalize
2. artificial neural network (ANN) or neural network
the resulting model from neural computing used for pattern recognition, forecasting, prediction, and classification
3. weights
provide the relative importance of each input in an artificial neural network
4. Artificial Neural Network
ANN
5. neurons
the main processing elements of a neural network
6. node
ANN equivalent of a soma
7. input
ANN equivalent of a dendrite
8. output
ANN equivalent of an axon
9. weight
ANN equibalent of a synapse
10. soma/node
middle of a neuron
11. dendrites/input
the portion of a network that receives signals
12. axon/output
the portion of a network responsible for sending signals to other cells
13. synapse/weight
the portion of a network that is able to alter signals by increasing or decreasing the strength of the connection
14. transformation (transfer) function
the function in an ANN that is responsible for the signal to "act" or "not"
allows all neurons to link the output in one layer to the input of the next layer, but does not allow any feedback linkage - the most commonly used network paradigm
16. network
organized neurons
17. topologies
network patterns of neurons
18. artificial neurons
the processing elements (PE) of an ANN which receives inputs, processes them, and delivers a single output
19. hidden layer
a layer of neurons that takes input from the previous layer and converts those inputs into outputs for further processing
20. processing elements
the "neuron" - performs the summation function
21. parallel processing
many processing elements perform their computations at the same time
22. input
represents a single element (ex: sales price) to be considered
23. inputs
several types of data, such as text, pictures, and voice, can be used as ____.
24. output
the solution to the problem - the purpose of the network computations - often in a 0 (no) or 1 (yes) format
25. connection weights
the key elements of an ANN - expfress the relative strength / mathematical value / importance of the input data or the many connections that transfer data from layer to layer
26. weights
through repeated adjustments of ____ a network learns
27. weights
store learned patterns of information
28. summation function
computes the weighted sums of all the input elements entering each processing element - multiplies each input value by its weight and totals the values for a weighted sum
29. transformation (transfer) function
combines (i.e., adds up) the inputs coming into a neuron from other neurons/sources and then produces an output
30. sigmoid (logical activativation) function
non-linear S-shaped transfer function in the range of 0 to 1
31. threshold value
a hurdle value for the output of a neuron to trigger the next level of neurons
32. supervised learning
neural network learning where a sample training set is used to "teach" the network about its problem domain
33. learning algorithm
determines how the neural interconnection weights are corrected due to differences in the actual and desired output for a member of the training set
34. supervised learning
type of ANN learning used for prediction-type problems - an exemplary data set is used against a learning algorithm that adjusts as it examines its own outcomes against predetermined preferred outcomes - data is labeled (good or bad)
35. unsupervised learning
type of ANN learning used for clustering-type problems - learns patterns by repeatedly examining data that is not labeled
36. learning rate
a parameter that controls the rate of learning (too high or too low a rate causes issues)
37. unserpervised learning
network does not try to learn a target answer, instead, it learns a pattern through repeated exposures
38. 1. collect organize & format the data
2. separate ate into training validation & testing sets
3. decide on a network architecture
4. select a learning algorithm
5. set network parameters & initialize their values
6. initialize weights & start training (& validation)
7. stop training, freeze the network weights
8. test the trained network
9. deploy the network for use on unknown new cases
steps of the deployment process of an ANN
39. sensitivity analysis
the inputs are perturbed (changed) within allowable value ranges while the relative change on the output is measured/recorded
40. sensitivity analysis
developed to help users understand how the model performs - conducted on a trained ANN - results illustrate the relative importance of input variables
41. logistical regression
the regression type that can handle a categorical outcome variable
 Author: mjweston ID: 237910 Card Set: Big Data - Exam II Updated: 2013-10-02 11:27:48 Tags: Artificial Neural Networks Folders: Description: Artificial Neural Networks Show Answers: