Deep Learning

Deep Learning - How the McCulloch-Pitts Neuron works

Introduction In this tutorial we will cover the very first and the simplest mathematical neuron model in history - the McCulloch-Pitts Neuron. We look at the architecture and functionality. History The McCulloch-Pitts-Neuron is the simplest form of a neuron model and was published in 1943 by Warren McCulloch and Walter...

AI Use Case - House Price Prediction with Neural Networks

Deep Learning - How Recurrent Neural Networks (RNNs) work: A Gentle Introduction

Introduction In contrast to a classic feedforward network, a Recurrent Neural Network (RNN) allows backward connections. An RNN is particularly suitable for processing time series data and is able to take into account the time dependency of data. This enables an RNN to have a kind of short-term memory. The...

Deep Learning - How Generative Adversarial Neural Neworks (GANs) work

Introduction In this tutorial, we want to explain how Generative Adversarial Neural Networks (GANs) work. In order to do this, we take a look at the functionality and the idea behind GANs. Principle Generative Adversarial Neural Networks (GANs) are a special type of Artificial Neural Networks. This kind of model...

AI Use Case - Penguin Species Prediction with Neural Networks

Introduction In this tutorial, we want to create an Artificial Neural Network (ANN) that is able to recognize the species of penguins based on their body measurements. We will cover the entire process from Data Collection, Data Cleansing and Feature Engineering to Model Creation and Model Training. Use Case Based...