EPISODE · Feb 15, 2017 · 1H
How to Simplify Your Dataset Easily (LIVE)
from Siraj Raval
We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle. Code for this video: https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE- Links from the video: https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/eigenvectors-and-eigenvalues/ More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
What this episode covers
We're going to compare some different techniques that reduce the dimensionality of our data so we can visualize it. We'll go through each one step by step including the math and I'll answer questions along the way. And I freestyle. Code for this video: https://github.com/llSourcell/How_to_Simplify_Your_Data-LIVE- Links from the video: https://georgemdallas.wordpress.com/2013/10/30/principal-component-analysis-4-dummies-eigenvectors-eigenvalues-and-dimension-reduction/ http://setosa.io/ev/eigenvectors-and-eigenvalues/ More learning resources: https://plot.ly/ipython-notebooks/principal-component-analysis/ http://sebastianraschka.com/Articles/2014_pca_step_by_step.html https://www.quora.com/What-is-the-difference-between-LDA-and-PCA-for-dimension-reduction https://www.quora.com/What-advantages-the-t-sne-algorithm-has-over-pca http://stats.stackexchange.com/questions/123040/whats-wrong-with-t-sne-vs-pca-for-dimensional-reduction-using-r https://www.oreilly.com/learning/an-illustrated-introduction-to-the-t-sne-algorithm Join us in the Wizards Slack channel: http://wizards.herokuapp.com/ Please subscribe! And like. And comment. That's what keeps me going. And please support me on Patreon: https://www.patreon.com/user?u=3191693 Follow me: Twitter: https://twitter.com/sirajraval Facebook: https://www.facebook.com/sirajology Instagram: https://www.instagram.com/sirajraval/ Instagram: https://www.instagram.com/sirajraval/
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How to Simplify Your Dataset Easily (LIVE)
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