How to Build Variational Autoencoder and Generate Images in Python

     Classical autoencoder simply learns how to encode input and decode the output based on given data using in between randomly generated latent space layer. By using this method we can not increase the model training ability by updating parameters in learning.

    The variational autoencoders, on the other hand, apply some statistical findings by using learned mean and standard deviations to learn the distribution. The latent space mean and variance are kept to update in each layer and this helps to improve the generator model.
    
    In this tutorial, we'll learn how to build the Variational Autoencoders (VAE) and generate the images with Keras in Python. The tutorial covers:
  1. Preparing the data
  2. Defining the encoder
  3. Defining generator
  4. Defining the VAE model
  5. Generating images
  6. Source code listing  

Regression Example with Nu Support Vector Regression Method in Python

   Based on support vector machines method, Nu Support Vector Regression (NuSVR) is an algorithm to solve the regression problems. The NuSVR algorithm applies nu parameter by replacing the the epsilon parameter of SVR method. The Scikit-learn explains that the parameter nu is an upper bound on the fraction of training errors and a lower bound of the fraction of support vectors¹.

   In this tutorial, we'll briefly learn how to fit and predict regression data by using Scikit-learn's NuSVR class in Python. The tutorial covers:
  1. Preparing the data
  2. Training the model
  3. Predicting and accuracy check
  4. Boston dataset prediction
  5. Source code listing
   We'll start by loading the required libraries.

Classification Example with Linear SVC in Python

   The Linear Support Vector Classifier (SVC) method applies a linear kernel function to perform classification and it performs well with a large number of samples. If we compare it with the SVC model, the Linear SVC has additional parameters such as penalty normalization which applies 'L1' or 'L2' and loss function. The kernel method can not be changed in linear SVC, because it is based on the kernel linear method. 

   In this tutorial, we'll briefly learn how to classify data by using Scikit-learn's LinearSVC class in Python. The tutorial covers:
  1. Preparing the data
  2. Training the model
  3. Predicting and accuracy check
  4. Iris dataset classification example
  5. Video tutorial
  6. Source code listing
   We'll start by loading the required libraries.

from sklearn.svm import LinearSVC
from sklearn.datasets import load_iris
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report

Classification Example with Support Vector Classifier (SVC) in Python

   Support Vector Machines (SVM) is a widely used supervised learning method and it can be used for regression, classification, anomaly detection problems. The SVM based classier is called the SVC (Support Vector Classifier) and we can use it in classification problems. It uses the C regularization parameter to optimize the margin in hyperplane and it is also called C-SVC. 

   In this tutorial, we'll briefly learn how to classify data by using Scikit-learn's SVC class in Python. The tutorial covers:
  1. Preparing the data
  2. Training the model
  3. Predicting and accuracy check
  4. Iris dataset classification example
  5. Source code listing
   We'll start by loading the required libraries.

from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.metrics import confusion_matrix from sklearn.metrics import classification_report

Nu-Support Vector Classification Example in Python

   Support Vector Machines (SVM) is a supervised learning method and it can be used for regression and classification problems. The SVM based classier is called the SVC (Support Vector Classifier) and we can use it in classification problems.
     The nu-support vector classifier (Nu-SVC) is similar to the SVC with the only difference that the nu-SVC classifier has a nu parameter to control the number of support vectors.

   In this tutorial, we'll briefly learn how to classify data by using Scikit-learn's NuSVC class in Python. The tutorial covers:
  1. Preparing the data
  2. Training the model
  3. Predicting and accuracy check
  4. Video tutorial
  5. Source code listing
   We'll start by loading the required libraries.

from sklearn.svm import NuSVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report

How to Build Variational Autoencoders and Generate Images in R

    In this tutorial, we'll learn how to build the Variational Autoencoders (VAE) and generate the images in R. Classical autoencoder simply learns how to encode input and decode the output based on given data using in between randomly generated latent space layer. By using this method we can not increase the model training ability by updating parameters in learning.


    The variational autoencoders, on the other hand, apply some statistical findings by using learned mean and standard deviations to learn the distribution. The latent space mean and variance are kept to update in each layer and this helps to improve the generator model. The tutorial covers,

  1. Preparing the data
  2. Defining the encoder
  3. Defining the VAE model
  4. Defining generator
  5. Generating images
  6. Source code listing