Beta-VAE is a very classical model for disentangled representation learning, the use of an expanding bottleneck that allow information into the decoder gradually is key to representation disentanglement as well as high-quality reconstruction. During recent experiments on such fascinating structure, we discovered that the total amount of latent variables can affect the representation learnt by the network: with very few latent variables, the network tend to learn the most important or principal variables, acting like a PCA; with very large numbers of latent variables, the variables tend to be more disentangled, and act like an ICA. Our assumption is that the competition between latent variables while trying to gain the most information bandwidth can lead to this phenomenon.

All the references are taken from the book -- Hands On Machine Learning with Scikit-learn, Keras & Tensorflow by Aurelien Geron. Notebook for this article can be found here. Support Vector Machines can be used for linear or non-linear classification, regression and even outlier detection. It is well suited for complex-small or medium-sized datasets. SVMs are also sensitive to feature scaling, if the feature are standardized it will generalize better.

The important job that SVM's perform is to find a decision boundary to classify our data. This decision boundary is also called the hyperplane. Lets start with an example to explain it. Visually, if you look at figure 1, you will see that it makes sense for purple line to be a better hyperplane than the black line. The black line will also do the job, but skates a little to close to one of the red points to make it a good decision line.

Learn to build a Polynomial Regression model to predict the values for a non-linear dataset. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

In previous stories, I have given a brief of Linear Regression and showed how to perform Simple and Multiple Linear Regression. In this article, we will go through the program for building a Polynomial Regression model based on the non-linear data. In the previous examples of Linear Regression, when the data is plotted on the graph, there was a linear relationship between both the dependent and independent variables. Thus, it was more suitable to build a linear model to get accurate predictions. What if the data points had the following non-linearity making the linear model giving an error in predictions due to non-linearity? In this case, we have to build a polynomial relationship which will accurately fit the data points in the given plot.

Support Vector Machine(SVM) is a powerful classifier that works with both linear and non-linear data. If you have a n-dimensional space, then the dimension of the hyperplane will be (n-1). The goal of SVM is to find an optimal hyperplane that best separates our data so that distance from the nearest points in space to itself is maximized. To keep it simple, consider a road, which separates the left, right-side cars, buildings, pedestrians and makes the widest lane as possible. And those cars, buildings, really close to the street are the support vectors.

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