# Classifying genres of movies by looking at the poster - A neural approach

In this article, we will apply the concept of multi-label multi-class classification with neural networks from the last post, to classify movie posters by genre. First we import the usual suspects in python. import numpy as np import pandas as pd import glob import scipy.misc import matplotlib %matplotlib inline import matplotlib.pyplot as plt …and then we import the movie metadata. path = 'posters/' data = pd.read_csv("MovieGenre.csv", encoding="ISO-8859-1") Now have a look at it.

# Guide to multi-class multi-label classification with neural networks in python

Often in machine learning tasks, you have multiple possible labels for one sample that are not mutually exclusive. This is called a multi-class, multi-label classification problem. Obvious suspects are image classification and text classification, where a document can have multiple topics. Both of these tasks are well tackled by neural networks. A famous python framework for working with neural networks is keras. We will discuss how to use keras to solve this problem.

# Getting started with Multivariate Adaptive Regression Splines

In this post we will introduce multivariate adaptive regression splines model (MARS) using python. This is a regression model that can be seen as a non-parametric extension of the standard linear model. The multivariate adaptive regression splines model MARS builds a model of the from $$f(x) = \sum_{i=0}^k c_i B_i(x_i),$$ where $x$ is a sample vector, $B_i$ is a function from a set of basis functions (later called terms) and $c_i$ the associated coefficient.