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 →

# Transfer Learning

Transfer Learning is the reuse of a pre-trained model on a new problem. It is currently very popular in the field of Deep Learning because it enables you to train Deep Neural Networks with comparatively little data. This is very useful since most real-world problems typically do not have millions of labeled data points to train... 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 →

# How to build a Neural Network with Keras

Keras is one of the most popular Deep Learning libraries out there at the moment and made a big contribution to the commoditization of artificial intelligence. It is simple to use and it enables you to build powerful Neural Networks in just a few lines of code. In this post, you will discover how you can... Continue Reading →

# Linear Algebra for Deep Learning

The concepts of Linear Algebra are crucial for understanding the theory behind Machine Learning, especially for Deep Learning. It gives you a better intuition for how algorithms really work under the hood, which enables you to make better decisions. So if you really want to be a professional in this field, you will not come around mastering some... Continue Reading →

# Data Types in Statistics

Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis (EDA) on your dataset,... Continue Reading →

# Gradient Descent

Gradient descent is by far the most popular optimization strategy, used in machine learning and deep learning at the moment. It is used while training your model, can be combined with every algorithm and is easy to understand and implement. Therefore, everyone who works with Machine Learning should understand it’s concept. After reading this posts... Continue Reading →

# Recurrent Neural Networks

Recurrent Neural Networks are the state of the art algorithm for sequential data and among others used by Apples Siri and Googles Voice Search. This is because it is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for Machine Learning problems that involve sequential data. It is... Continue Reading →