Is this you? As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
Claim your profile
Articles by Patrick Tinz
Univariate Discrete Distributions: An Easy-to-Understand Explanation
Understand univariate discrete distributions mathematically and visually Do you know this feeling? You want to learn something new, but you don’t know where to start. This is how we felt when we wanted to understand distributions mathematically. Yes, our professor explained all these distributions to us, but only mathematically, with formulas! Our professor didn’t explain it in an easy-to-understand way with visuals.
Understand and Implement an Artificial Neural Network from Scratch
Artificial intelligence is a hype topic. But what is behind the powerful networks of the big tech companies? An artificial neural network (ANN) is usually implemented with frameworks such as TensorFlow, Keras or PyTorch. Such frameworks are suitable for very complex ANNs. As a data scientist, however, it is essential to understand the basics. This article aims to help you understand how a neural network works. First, we introduce the basics of an ANN in general.
Efficient Testing of Your Python Code With Pytest
TESTING AND PROGRAMMING Do you often look for errors in software components that could prevent with a better testing strategy? Sometimes you or a colleague change a small thing in existing software, and a few weeks later, you realize that a certain functionality no longer works. It is frustrating and tedious to find the old bugs. You can avoid all this by testing the functions and classes of your code.
A beginner-friendly introduction to cross-validation
By Janik and Patrick Tinz An explanation of three different types of cross-validation with Python examples Photo by Richard Horvath on Unsplash Cross-validation (CV) is a statistical test procedure based on resampling. It is an essential tool in modern statistics. Resampling refers to repeatedly taking samples from a training dataset and fitting a model to each sample again. This...
Detection of Credit Card Fraud with an Autoencoder
A guide for the implementation of an anomaly detector Do you want to know how to create an anomaly detector using Python and TensorFlow? Then this article is for you. Credit card companies use anomaly detectors to detect fraudulent transactions. It is important to identify fraudulent transactions so that customers do not have to pay for something they did not buy. Many credit card transactions take place every day, but very few transactions are fraudulent. The fraudulent transactions are anomalies.
Data Science Project Mastery through KPIs Original
Data Science Project Mastery through KPIs Use of key performance indicators in the controlling of data science projects Photo by Alexandr Podvalny on Unsplash Project controlling is essential in data science projects, as the time required and costs are often difficult to estimate. Clients want to implement their use cases efficiently and successfully with machine learning methods. You can positively influence consistent…
A Comprehensive Guide to Building Enterprise-Level Plotly Dash Apps
Production ready web apps with pure Python and Docker Data Science projects always need some kind of visualisation. For the initial analysis, data scientists usually use Jupyter Notebooks and libraries like matplotlib or seaborn. In exploratory data analysis, data scientists use histograms, and scatter plots or make statistical evaluations. For the first insights, this approach is very well suited. However, interactive dashboards are more appropriate for presenting the results.
Tips and Tricks to improve your R-Skills
Learn how to write efficient R code R is widely used in business and science as a data analysis tool. The programming language is an essential tool for data driven tasks. For many Statisticians and Data Scientists, R is the first choice for statistical questions. Data Scientists often work with large amounts of data and complex statistical problems. Memory and runtime play a central role here. You need to write efficient code to achieve maximum performance.
Unlock the Full Potential of Python
Tips and Tricks for Readable and Efficient Code Python is one of the most popular programming languages among developers and Data Scientists. Developers use the language for backend and frontend development. Data Scientists and Analysts use Python to analyse data. The main machine-learning libraries are available in Python. For this reason, Python is very popular among Data Scientists. In this article, we show some Python tips and tricks.
Unlock the Full Potential of Python
Python is one of the most popular programming languages among developers and Data Scientists. Developers use the language for backend and frontend development. Data Scientists and Analysts use Python to analyse data. The main machine-learning libraries are available in Python. For this reason, Python is very popular among Data Scientists. In this article, we show some Python tips and tricks. The tips and tricks will help you to make your Python code more efficient and readable.
Deploy Your Own MLflow Workspace On-Premise with Docker
Manage the lifecycle of your ML models like a Pro MLflow is an open source platform to manage the lifecycle of ML models end to end. It tracks the code, data and results for each ML experiment, which means you have a history of all experiments at any time. A dream for every Data Scientist. Moreover, MLflow is library-agnostic, which means you can use all ML libraries like TensorFlow, PyTorch or scikit-learn. All MLflow functions are available via a REST API, CLI, Python API, R API and Java API.
Speed Up your Python Skills in 2023
Seven tips to take you to the next level Python is the most widely used programming language in the data science domain, and its popularity continues to grow. The entire data science field has grown enormously in recent years. In this article, we will show you seven tips on how to improve your Python skills. It’s often the little things that make a big difference. The tips will enrich your life as a Data Scientist. That’s why we give you seven tips that you can put into practice right now.
Speed Up your Python Skills in 2023
NumPy is a Python library to work efficiently with arrays. It also offers fast and optimised vectorised operations. But! It does not support parallel processing. As an alternative to NumPy, you can use NumExpr. NumExpr achieves significantly better performance than NumPy because it supports multi-threading. Furthermore, it avoids allocating memory for intermediate results. First, you have to install the packages NumPy and NumExpr.
Show More
loading
Actions
Is this you?
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.Get in touch with Patrick
Contact Patrick, search articles and posts on X, monitor coverage, and track replies from one place.
Learn more about Muck Rack