{"id":25021,"date":"2021-01-12T08:30:10","date_gmt":"2021-01-12T08:30:10","guid":{"rendered":"http:\/\/onlineclassesguru.com\/?p=25021"},"modified":"2021-01-12T08:30:10","modified_gmt":"2021-01-12T08:30:10","slug":"design-and-train-a-neural-network-to-accomplish-some-classification-task-for-this-milestone-you-will-design-and-train-a-neural-network-to-accomplish-some-classification-task","status":"publish","type":"post","link":"https:\/\/onlineclassesguru.com\/index.php\/2021\/01\/12\/design-and-train-a-neural-network-to-accomplish-some-classification-task-for-this-milestone-you-will-design-and-train-a-neural-network-to-accomplish-some-classification-task\/","title":{"rendered":"Design and train a neural network to accomplish some classification task. For this milestone, you will design and train a neural network to accomplish some classification task."},"content":{"rendered":"<style type=\"text\/css\"><\/style><h4>Design and train a neural network to accomplish some classification task.<br \/>\nFor this milestone, you will design and train a neural network to accomplish some classification task.<\/h4>\n<p>Choose a data set<br \/>\nThe UCI Machine Learning Archive hosts various data sets suitable for testing learning algorithms. I suggest clicking on \u201cView ALL Data Sets\u201d on the right side of the page. That provides a nice interface in which you can filter by data type or area of interest.<\/p>\n<p>The data should be suitable for a classification task, not clustering, recommendations, or regression. Neural networks support both categorical and numerical data, you\u2019ll just want to keep the number of attributes to less than 100, because we\u2019ll have to tune the way each attribute is presented to the network.<\/p>\n<p>When you click on the data set, you\u2019ll see a description, citations, and details about the attributes. There are links near the top to the \u201cData Folder\u201d, and there you\u2019ll find a list of files ending in .data (the raw data) or .names (attribute descriptions).<\/p>\n<p>Download the data and descriptions. I have a lot of experience with the Mushroom data, so I\u2019ll explore that in this explanation \u2013 but you can choose something else for your project. For mushrooms, the .names file contains:<\/p>\n<p>1. Title: Mushroom Database<\/p>\n<p>2. Sources:<br \/>\n(a) Mushroom records drawn from The Audubon Society Field Guide to North<br \/>\nAmerican Mushrooms (1981). G. H. Lincoff (Pres.), New York: Alfred<br \/>\nA. Knopf<br \/>\n(b) Donor: Jeff Schlimmer (Jeffrey.Schlimmer@a.gp.cs.cmu.edu)<br \/>\n(c) Date: 27 April 1987<\/p>\n<p>3. Past Usage:<br \/>\n1. Schlimmer,J.S. (1987). Concept Acquisition Through Representational<br \/>\nAdjustment (Technical Report 87-19). Doctoral disseration, Department<br \/>\nof Information and Computer Science, University of California, Irvine.<br \/>\n\u2014 STAGGER: asymptoted to 95% classification accuracy after reviewing<br \/>\n1000 instances.<\/p>\n<p>[etc.]<br \/>\n5. Number of Instances: 8124<\/p>\n<p>6. Number of Attributes: 22 (all nominally valued)<\/p>\n<p>7. Attribute Information: (classes: edible=e, poisonous=p)<br \/>\n1. cap-shape: bell=b,conical=c,convex=x,flat=f,<br \/>\nknobbed=k,sunken=s<br \/>\n2. cap-surface: fibrous=f,grooves=g,scaly=y,smooth=s<br \/>\n3. cap-color: brown=n,buff=b,cinnamon=c,gray=g,green=r,<br \/>\npink=p,purple=u,red=e,white=w,yellow=y<\/p>\n<p>[etc.]<br \/>\nThe .data file is a text file with comma-separated values (CSV), which can be imported easily into Excel or other spreadsheet applications:<\/p>\n<p>p,x,s,n,t,p,f,c,n,k,e,e,s,s,w,w,p,w,o,p,k,s,u<br \/>\ne,x,s,y,t,a,f,c,b,k,e,c,s,s,w,w,p,w,o,p,n,n,g<br \/>\ne,b,s,w,t,l,f,c,b,n,e,c,s,s,w,w,p,w,o,p,n,n,m<br \/>\np,x,y,w,t,p,f,c,n,n,e,e,s,s,w,w,p,w,o,p,k,s,u<br \/>\ne,x,s,g,f,n,f,w,b,k,t,e,s,s,w,w,p,w,o,e,n,a,g<br \/>\n[etc.]<br \/>\nDesign your network<br \/>\nYour next task is to design your neural network architecture: how many neurons in each layer, and how to map neuronal activations to and from the data set?<\/p>\n<p><center><a href=\"http:\/\/onlineclassesguru.com\/orders\/ordernow\"><img decoding=\"async\" src=\"https:\/\/encrypted-tbn0.gstatic.com\/images?q=tbn:ANd9GcTyj99p60XCLyLk1htB7-1neRt8-2QdnenNlQ&usqp=CAU\"target=\"_http:\/\/onlineclassesguru.com\/orders\/ordernow\"\/><\/center><p>","protected":false},"excerpt":{"rendered":"<p>Design and train a neural network to accomplish some classification task. For this milestone, you will design and train a neural network to accomplish some classification task. Choose a data set The UCI Machine Learning Archive hosts various data sets suitable for testing learning algorithms. I suggest clicking on \u201cView ALL Data Sets\u201d on the&#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-25021","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v17.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Design and train a neural network to accomplish some classification task. 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