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Niklas Donges

About Me

Niklas Donges is Machine Learning Engineer at SAP. He is a Technical Blogger for the 'Towards Data Science' publication

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 build a Neural Network with Keras that predicts the sentiment of user reviews by categorizing them into two categories: positive or negative. 

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 such complex models. This blog post is intended to give you an overview of what Transfer Learning is, how it works, why you should use it and when you can use it. 

The Logistic Regression Algorithm

Logistic Regression is one of the most used Machine Learning algorithms for binary classification. In this post, you will learn what Logistic Regression is and how it works. You can have a solid understanding of its advantages and disadvantages and know when you can use it. Also, you can discover ways to use Logistic Regression to do multiclass classification with sklearn and why it is a good baseline to compare other Machine Learning algorithms with 

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 high classification accuracy is in most cases not as desirable as it sounds, what the right evaluation metrics are and when you should use them. You will also discover how you can create a classifier with virtually any precision you want.

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 called “Agile”. This is a flexible approach based on delivering requirements iteratively and incrementally throughout the project life cycle. This post, will give you a gentle introduction to agile and non-agile project management approaches with the focus on the Scrum Methodology. 

Pros and Cons of Neural Networks

Neural networks are great for some tasks but not as great for others. Huge amounts of data, more computational power, better algorithms and intelligent marketing increased the popularity of Deep Learning and made it into one of the hottest fields right now. On top of that, Neural Networks can beat nearly every other Machine Learning algorithms and the disadvantages that go along with it. The biggest disadvantages are their „black box“ nature, increased duration of development (depending on your problem), the required amount of data and that they are mostly computational expensive.

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 you will understand how Gradient Descent works, what types of it are used today and what are their advantages and tradeoffs.

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. In this post, discover the different data types that are used throughout statistics. Learn the difference between discrete & continuous data and learn what nominal, ordinal interval and ratio measurement scales are.  Know what statistical measurements you can use at which datatype and which are the right visualization methods. This enables you to create a big part of an exploratory analysis on a given dataset

Linear Algebra for Deep Learning

In this post, you will learn about the mathematical objects of Linear Algebra that are used in Machine Learning. You will also learn how to multiply, divide, add and subtract these mathematical objects.  You will also learn about the most important properties of Matrices and why they enable us to make more efficient computations. On top of that, you will learn what inverse- and transpose Matrices are and what you can do with it. Although there are also other parts of Linear Algebra used in Machine Learning, this post gives you a proper introduction to the most important concepts.

Introduction to Descriptive Statistics

Descriptive Statistical Analysis helps you to understand your data and is a very important part of Machine Learning. This is due to Machine Learning is all about making predictions. On the other hand, statistics is all about drawing conclusions from data, which is a necessary initial step. In this post, you will learn about the most important descriptive statistical concepts. They will help you understand better what your data is trying to tell you, which will result in an overall better machine learning model and understanding.

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