Did you want to know how to build a web frontend in Python? In the 4th and last part of our blog series, we develop an interactive game. The user has to guess the car brand and model. Do you think you can beat our AI?
In the third part of our blog series, we cover an essential topic that has gained significant traction in the ML-community in the past years: Explainability. Explainable AI is essential to establish trust in the models we develop. We discuss various approaches for CNN networks, with a particular focus on the Grad-CAM method.
How do we interact with machine learning models in practice? In the second part of our 4-part blog series on car model classification, you will learn how models can be deployed using TensorFlow Serving, and how we can run model queries.
In this 4-part blog series on car model classification, we want to illustrate how an end-to-end deep learning project can be implemented. In the first part, we will show how you can use transfer learning to tackle car image classification. In particular, you will learn how a pre-trained ResNet model can be fine-tuned to tackle a downstream task.
We are encountering Machine Learning algorithms in our daily lives. Some are practical, like Google Translate; others are fun, like Snapchat Filters. Our interaction with artificial intelligence will most likely increase in the next few years. Given the potential impact of Machine Learning models on our future lives, Fran Peric presents to you the five branches of Machine Learning and their key concepts.
The foundations for image recognition and computer vision were already laid down in the 1970s. However, it is only in recent years that the field has found increasing application outside research. This article presents five selected and particularly promising use cases from different industries, which are either already in production or promise significant changes in their respective fields in the coming years.
NLP (Natural Language Processing) generally describes the computer-aided processing of human language. This includes both written and spoken language. In this article, we show different NLP case studies and explain how, within only a few years, the barriers to market entry have been lowered to such an extent that today every company can use NLP to solve their individual business problems.
A few weeks ago, several of our colleagues here at STATWORX participated in an exciting time series competition, hosted by a large german-based company.
This was the opportunity for us to organize an internal hackathon in our office in Frankfurt. In this blog article, Marlon will give you an insight into how we plan and execute such hackathons at STATWORX.