ML: Classification - Githubissues

Introduction I’ve always had a passion for learning and consider myself a lifelong learner. Being at SAS, as a data scientist, allows me to learn and try out new algorithms and functionalities that we regularly release to our customers. Often times, the algorithms are not technically new, but they’re new to me which makes it a lot of fun.

Recently, I had the opportunity to learn more about t-Distributed Stochastic Neighbor Embedding (t-SNE). In this post I’m going to give a high-level overview of the t-SNE algorithm. I’ll also share some example python code where I’ll use t-SNE on both the Digits and MNIST dataset.

What is t-SNE? t-Distributed Stochastic Neighbor Embedding (t-SNE) is an unsupervised, non-linear technique primarily used for data exploration and visualizing high-dimensional data. In simpler terms, t-SNE gives you a feel or intuition of how the data is arranged in a high-dimensional space. It was developed by Laurens van der Maatens and Geoffrey Hinton in 2008.

t-SNE vs PCA If you’re familiar with Principal Components Analysis (PCA), then like me, you’re probably wondering the difference between PCA and t-SNE. The first thing to note is that PCA was developed in 1933 while t-SNE was developed in 2008. A lot has changed in the world of data science since 1933 mainly in the realm of compute and size of data. Second, PCA is a linear dimension reduction technique that seeks to maximize variance and preserves large pairwise distances. In other words, things that are different end up far apart. This can lead to poor visualization especially when dealing with non-linear manifold structures. Think of a manifold structure as any geometric shape like: cylinder, ball, curve, etc.

t-SNE differs from PCA by preserving only small pairwise distances or local similarities whereas PCA is concerned with preserving large pairwise distances to maximize variance. Laurens illustrates the PCA and t-SNE approach pretty well using the Swiss Roll dataset in Figure 1 [1]. You can see that due to the non-linearity of this toy dataset (manifold) and preserving large distances that PCA would incorrectly preserve the structure of the data.

Visualizing PCA with Leaf Dataset https://www.kaggle.com/selfishgene/visualizing-pca-with-leaf-dataset

Introduction to Decision Trees (Titanic dataset) https://www.kaggle.com/dmilla/introduction-to-decision-trees-titanic-dataset

Regression Types https://mindmajix.com/lasso-regression

philusnarh commented 5 years ago

Supervised Machine Learning: Classification https://towardsdatascience.com/supervised-machine-learning-classification-5e685fe18a6d

PyAstronomy https://www.hs.uni-hamburg.de/DE/Ins/Per/Czesla/PyA/PyA/funcFitDoc/tutorialMCMC.html

EMCEE http://dfm.io/emcee/current/user/line/

An Introduction to Feature Selection https://machinelearningmastery.com/an-introduction-to-feature-selection/

philusnarh commented 5 years ago

AutoEncoder

https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/2vous47vib4a55o/autoencoder1.ipynb#

https://nbviewer.jupyter.org/urls/dl.dropbox.com/s/oflj51a252tig83/image_denoising_c_autoencoder.ipynb

philusnarh / PROJECT

ML: Classification #22

Keras Models