Introduction Autoencoders, a cornerstone of unsupervised learning, have emerged as a powerful tool in the domain of artificial intelligence. By compressing and then reconstructing input data, these neural networks uncover meaningful representations, paving the way for applications ranging from image denoising to anomaly detection. In this tutorial we want to...
Introduction Traditional RNNs, limited by their simplistic structure, have problems retaining information over longer time periods, leading to the infamous vanishing gradient problem. Long Short-Term Memory (LSTM) Networks have the impressive ability to capture and preserve long-term dependencies in sequential data. But how is an LSTM able to do this?...
Introduction In 1997, Sepp Hochreiter and Jürgen Schmidhuber introduced Long Short-Term Memory (LSTM), a special type of Recurrent Neural Network (RNN). The motivation was to overcome the problems of traditional RNNs when dealing with long sequences. This is exactly what LSTM Networks (LSTMs) are supposed to do. The special architecture...
Introduction In this tutorial we will cover how to represent boolean functions with the McCulloch-Pitts Neuron. We look at the boolean functions AND, OR and NOT. McCulloch-Pitts Neuron We have already explained how the McCulloch-Pitts Neuron works in another post. If you haven't seen it yet, check it...
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...
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...
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...
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...