Deep Learning in Keras – Data Preprocessing
Introduction
Deep learning is one of the most interesting and promising areas of artificial intelligence (AI) and machine learning currently. With great advances in technology and algorithms in recent years, deep learning has opened the door to a new era of AI applications.
In many of these applications, deep learning algorithms performed equal to human experts and sometimes surpassed them.
Python has become the go-to language for Machine Learning and many of the most popular and powerful deep learning libraries and frameworks like TensorFlow, Keras, and PyTorch are built in Python.
In this series, we’ll be using Keras to perform Exploratory Data Analysis (EDA), Data Preprocessing and finally, build a Deep Learning Model and evaluate it.
Data Preprocessing
In the preprocessing stage, we’ll prepare the data to be fed to the Keras model. The first step is clearing the dataset of null values. Then, we’ll use one-hot encoding to convert categorical variables to numerical variables. Neural Nets work with numerical data, not categorical.
We’ll also split the data into a training and testing set. Finally,