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In this stage, you will design and write a small website with a topic of your own choice consisting of
webpages using Hypertext Markup Language (HTML5) code. You are to develop a basic website, which
provides information in a clear way and links to other pages.
You are to demonstrate your understanding of the correct use of HTML tags. Use both the code and
design view, if you are using MS Expression Web editor. It will be obvious to your marker if you only
used the design view, as it shows up in the code structure. Automatically generated code looks
different from manually created HTML code. We will look for evidence that you either manually coded
the webpages or that you manually corrected the code generated by MS Expression Web or a similar
software.
Part III of this document has a sample case study that we’ll go through during the tutorial. For this first
stage, you will need to follow the same steps of the case study but starting to use your own topic.
Your topic is free for you to choose (a hobby? Small business?). However, extra marks will be awarded
(see marking criteria) if you decide to pick any of the following:
• Indigenous Australians.
• Australia’s Wildlife.
These are very broad topics, so please talk with your lecturer/tutor to narrow them down.

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Neural Net Evolution. A neural net typically starts out with random weights; hence it produces essentially random predictions in the first iteration. Describe how the neural net evolves (in JMP) to produce a more accurate prediction? Car Sales. Consider again the data on used cars ( ) with 1436 records and details on 38 attributes, including Price, Age, KM, HP, and other specifications. The goal is to predict the price of a used Toyota Corolla based on its specifications. a. Determine which variables to include, and use the neural platform in JMP Pro to fit a model. Use the validation column for validation, and use the default values in the Neural model launch dialog. Record the RMSE for the training data and the validation data, and save the formula for the model to the data table (use the Save Fast Formulas option, which will save the formula as one column in the data table). Repeat the process, changing the number of nodes (and ouly this) to 5, 10, and 25. i. Using your recorded values, what happens to the RMSE for the training data as the number of nodes increases? ii. What happens to the RMSE for the validation data? iii. Comment on the appropriate number of nodes for the model. iv. Use the Model Comparison platform to compare these four models (use the Validation column as either aByvariable or as a Group variable, and focus only on the validation data). Here, RASE is reported rather than RMSE. Compare RASE and AAE (average absolute error) values for these four models. Which model has the lowest “error”? b. Conduct a sintilar experiment to assess the effect of changing the number oflayers in the network as well as the activation functions.

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