Reference no: EM131705837 , Length: 10

Assignment: Neural Network

1. Introduction -

Inspired by human neural biology, neural networks (NN) were first envisaged by psychologists and neurologists in the 1940s. NNARE once again experiencing resurgence in data science and machine learning.

In simple terms, an NN is a connected network of nodes or neurons representing input and output, along with appropriate weights associated with connections between neurons.  NN are used in many applications such as image and speech recognition, robotics, numerical control, game playing, cancer diagnostics, and more. NN can employ both as supervised and unsupervised learning techniques. There are a number of different NN architectures and algorithms available, one popular algorithm is known as back-propagation.

2. Steps to Completion -

For each study the general procedure is to:

• Review theoretical background based on available resources in the course content
• Select a dataset from the module's recommended datasets list
• Run an analysis, perform evaluation, and capture the results
• Document your findings and analysis in a data mining analytical report

3. Deliverables -

Submit your analysis report by addressing the following critical areas:

Introduction: give some background and context about the domain of application, provide the rationale for the type of analysis, and state the objective clearly.

Analysis: describe the data both qualitatively and quantitatively through exploratory analysis, perform necessary preprocessing activities, give some intuition about the algorithm and core parameters, demonstrate the model building steps along with parameter tuning, and explain all your assumptions.

Result: explain the result and interpret the model output using terms that reflect the application area, perform model evaluation using the appropriate metrics, and leverage visualization.

Conclusion: summarize your main findings, discuss experimental limitations related to the data and/or implementation of the algorithm, and suggest improvement areas as a potentiation future work.

Miscellaneous:

• Proof read your report for correct structure, grammar, and spelling
• Follow appropriate APA formatting and provide all references
• Include your R script and extended model outputs in an Appendix section.