Answer 1
The ANN creation process is almost an equivalent of the look methodologies structured by ancient computer-based information systems; certain steps are distinctive or can be entirely different.
1. Gathering Data
During this step, the information to be used for coaching and testing is collected. The information gathered should be useful and beneficial for the need for a neutral network project to be carried out.
2. Coaching Data Recognition
The coaching information is known in this phase, and the concept of keeping out the network’s coaching and testing success is unreal.
3. Network Architecture Selection
The third phase includes selecting requirements. This means that the resources provided are reviewed to see whether they are appropriate for the network to be built or not.
4. Learning technique collection
The educational approach is selected in step four. It should be noted that different learning methods are suitable for different networks, so the educational approach that fits your network must be selected.
5. Formatting of Network parameters and Weights
The network weights are initialized, and because the feedback on coaching results is obtained, the parameters are also altered. In deciding the duration and potency of coaching, it must be remembered that the original principles are forever significant. Again, as the coaching continues to be carried out, a lot of the techniques that are implemented change the parameters.
6. Conversion of Application Data
In step six, the information that is used is remodelled into the format and type that the neural network requires.
7. Step Seven and Eight
The testing and coaching are carried out in steps seven and eight by providing the input or required knowledge to the network.
9. Acquisition of a stable weight set
That is, in step 9, a stable weight set is obtained in the last step.
Akanni, O. O. (2013). Project Selection: Artificial Neural Network Approach. Science Journal of Business and Management, 1(3), 37. https://doi.org/10.11648/j.sjbm.20130103.11
De Wilde, P. (1998). A neural network model of a communication network with information servers. Neural Computing & Applications, 7(1), 2636. https://doi.org/10.1007/bf01413707
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Answer 2
Nine Steps
These are the nine steps for a neural network project.
Raw or original data is collected from wide variety of sources. Data is accumulated from observation, interviews, discussions, surveys, research, etc.
Raw data can contain missing values, misaligned and in some cases contaminated. In this step raw data is cleansed, standardized, and transformed into the format that is compatible for the project.
Not all of the data collected is used for the project. In most cases data is reduced to independent data sets for all practicality purposes.
This phase details the choice of model architecture for the purpose like number of layers, number of nodes, arcs, degree of connectivity, etc.
Step 5 lays out the purpose of the project whether it may be for accuracy, precision, behavioral patterns, prediction, speed, agility, etc.
Training step consists of using optimization algorithm for feedforward inputs and map them with corresponding outputs.
Optimization phase includes the choice of optimization techniques like backpropagation, Levenberg-Maquardt, Conjugate Gradient, etc. This involves optimizing performance, accuracy, and precision of the analysis.
This is the testing phase of generating desired output or analysis from the input.
This is the last step where the research, analysis that is optimized, tested are put to action on the field with a stable dataset.
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