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You can still start with an easy one such as L2-regularized Logistic Regression, or k-means, but you should also push yourself to implement more interesting ones such as LDA (Latent Dirichlet Allocation) or SVMs. You can use a reference implementation in one of the many existing libraries to...
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Machine vision.While the ability to process and interpret visual information comes very intuitively to humans, translating our abilities to an algorithmic environment in this matter has proven to be an intimidating process. In fact, I’d argue that the largest obstacle facing self-driving cars is ...
Why do you think there is a better way? My first thought was also some kind of bilinear interpolation algorithm, but you don't seem to like that solution. Are you looking for something other than an interpolative algorithm (maybe something that involves multivariable regression), or are you ...
2. How Multiple Regression Analysis would be considered to examine the multivariable influence in correlation when ap List some of the ways Multivariate linear regression of the data are useful. Briefly describe how you would implement a dummy variable in a data...
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probability distributions will be relevant for different business models). The second approach is to do a regression analysis to create a line of best fit. You can accomplish this with a multivariable linear equation or a logarithmic trendline applied to your historical dataset (try this inExcel)...
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