The Non-Technical AI Guide

According to McKinsey, AI will create an estimated $13 trillion of GDP growth between now and 2030. As a comparison, the GDP of the entire United States of America was around 19 trillion in 2017. Leading AI scientists, like Andrew Ng, describe AI as the fourth industrial revolution or „the new electricity“. AI is undoubtedly... Continue Reading →

Connectionist Temporal Classification

Connectionist Temporal Classification (CTC) is a valuable operation to tackle sequence problems where timing is variable, like Speech and Handwriting recognition. Without CTC, you would need an aligned dataset, which in the case of Speech Recognition, would mean that every character of a transcription, would need to be aligned to its exact location in the... Continue Reading →

Agile and Non-Agile Project Management

Software project management is the practice of planning and executing software projects. Its concepts need to be understood by every team member to ensure a smooth project flow. There are different methodologies that can be mainly divided into structured and flexible approaches. The most common approach, which gained a lot of popularity in recent years, is... Continue Reading →

The Logistic Regression Algorithm

Logistic Regression is one of the most used Machine Learning algorithms for binary classification. It is a simple Algorithm that you can use as a performance baseline, it is easy to implement and it will do well enough in many tasks. Therefore every Machine Learning engineer should be familiar with its concepts. The building block... Continue Reading →

Pros and Cons of Neural Networks

Deep Learning enjoys a massive hype at the moment. People want to use Neural Networks everywhere, but are they always the right choice? That will be discussed in the following sections, along with why Deep Learning is so popular right now. After reading it, you will know the main disadvantages of Neural Networks and you... Continue Reading →

Evaluation Metrics for Classification

Using the right evaluation metrics for your classification system is crucial. Otherwise, you could fall into the trap of thinking that your model performs well but in reality, it doesn't. In this post, you will learn why it is trickier to evaluate classifiers, why a high classification accuracy is in most cases not as desirable... Continue Reading →

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