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Spotify curates a big playlist of all the music you listen to over the year. Here’s mine for 2018. (Note: makes for a great gym playlist)
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In my paper on Linear Regression (see Paper 2 ) we talked about developing a Linear Regression Model for timeseries data. Our group decided to use the Adam Optimizer and Gradient Descent models to compare the results from each. This code will go over the Adam Optimizer model. Lets go over the python! You can see the full python file here: linear_regression.py
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If you still maintain an Angular 1.x Application, here’s a not so well-known trick to help speed up performance.
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Advancement in technologies used in self-driving cars may allow for machine vision to surpass LIDAR…
Recommended citation: Newman, Krys. (2018). "Machine Vision for Safer Self-Driving Cars." http://krysnewman.com/files/machine-vision-paper1.pdf
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Least squares approximation and Adam Optimization were the methods…
Recommended citation: Newman, Krys. (2018). "Linear Regression." http://krysnewman.com/files/linear-regression-paper2.pdf
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Recurrent Neural Network computations allow for information to persist during computation through connected nodes forming a directed graph…
Recommended citation: Newman, Krys. (2018). "Time Series Forecasting with Neural Nets." http://krysnewman.com/files/time-series-nn-paper3.pdf
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Construction Zones are often very different from one another, making automatic road sign detection and navigation in these areas a challenge for self-driving cars….
Recommended citation: Newman, Krys. (2018). "Machine Vision for Safer Self Driving Cars in Construction Zones." http://krysnewman.com/files/machine-vision-construction-paper5.pdf