One of the most essential Key-Techniques in Deep Learning — In this article, I will introduce you to the theory and practical implementation of a very useful and effective technique called “batch normalization”. Batch normalization can significantly speed up the training of a neural network and lead to higher performance. 1. Introduction Neural networks learn to make a prediction for a given…

Using Deep Learning to predict how a customer will behave in the future — In this tutorial, I want to show you how deep neural networks can be used to predict the future behavior of people. This is usually referred to as Predictive Behaviour Modeling. In particular, we will discuss what Predictive Behavior Modeling actually means, in which business areas it is used, and…

Not sure if good model… or just overfitting? — In applied Deep Learning, we very often face the problem of overfitting and underfitting. This is a detailed guide that should answer the questions of what is Overfitting and Underfitting in Deep Learning and how to prevent these phenomena. In Short: Overfitting means that the neural network performs very well…

A Guide on the Concept of Loss Functions in Deep Learning — What they are, Why we need them… — This in-depth article addresses the questions of why we need loss functions in deep learning and which loss functions should be used for which tasks. In Short: Loss functions in deep learning are used to measure how well a neural network model performs a certain task. Table of Content Why…

A Guide on the Theory of Activation Functions in Neural Networks and why we need them in the first place. — In this detailed guide, I will explain everything there is to know about activation functions in deep learning. Especially what activation functions are and why we must use them when implementing neural networks. Short answer: We must use activation functions such as ReLu, sigmoid and tanh in order to add…

Analysis of the Technology of our Future — Trends, Projections, Opportunities — Artificial Intelligence is on the rise. The pace of growth for artificial intelligence within the consumer, enterprise, government, and defense sectors continues. In this article, we will analyze the current size of the AI market and make forecasts for the future. 1. Artificial Intelligence in the corporate Sector Let’s first take a look at the current…

Why do we need Stochastic, Batch, and Mini Batch Gradient Descent when implementing Deep Neural Networks? — This is a detailed guide that should answer the questions of why and when we need Stochastic-, Batch-, and Mini-Batch Gradient Descent when implementing Deep Neural Networks. In Short: We need these different ways of implementing gradient descent to address several issues we will most certainly encounter when training Neural…

A Guide on the Theory and Practicality of the most important Regularization Techniques in Deep Learning — Regularization is a set of techniques that can prevent overfitting in neural networks and thus improve the accuracy of a Deep Learning model when facing completely new data from the problem domain. In this article, we will address the most popular regularization techniques which are called L1, L2, and dropout.

A Guide on how to implement Neural Networks in TensorFlow 2.0 to detect anomalies. — In this detailed guide, I will explain how Deep Learning can be used in the field of Anomaly Detection. Furthermore, I will explain how to implement a Deep Neural Network Model for Anomaly Detection in TensorFlow 2.0. …