May
6,
2019
Deep Learning with Java: Introduction to Deeplearning4j
Deep learning is now often considered to be the "holy grail" when it comes to developing intelligent systems. While fully automatic and autonomous machine learning is on the way, current solutions still require the understanding of a software developer or engineer. Deep learning, by contrast, is a sub-discipline of machine...
Apr
24,
2019
Tutorial: Introduction to the R programming language
You already have some experience with SQL and are wondering how you could find solutions to problems in R? Then this article is just the thing you need! We’ll start with the basic elements of the language - with lots of specific sample code to help. Then we’ll take a...
Apr
1,
2019
Tutorial: An introduction to the Python programming language
When Guido van Rossum developed Python, he wanted to create a "simple" programming language that bypassed the vulnerabilities of other systems. Due to the simple syntax and sophisticated syntactic phrases, the language has become the standard for various scientific applications such as machine learning.
Mar
20,
2019
Infographic: Top 5 Python Libraries for Machine Learning
It is well-known in the Developer Scene that there is no better machine learning language than Python. One of the reasons why this programming language is so popular is the fact that it has a huge collection of great libraries, that makes the life of a developer a lot easier....
Feb
19,
2019
TensorFlow.js: What is possible with Machine learning in the browser?
When you talk about Machine Learning and Google's TensorFlow, most people think of Python and specialized hardware rather than JavaScript and any browser. This article explains what TensorFlow.js can do and why it makes sense to run machine learning in a browser.
Feb
8,
2019
Keynote http://commodity.ai
How can AI be turned into a commodity – a cheap, easily available product, that is used by everyone? Will it even be possible to turn AI into a commodity at all? Dr. Pieter Buteneers (Robovision) adresses these questions in this keynote from ML Conference 2018 in Berlin.
Nov
16,
2018
AI as a smart services for everyone
If you cannot or do not want to build an AI project from scratch, you have countless choices of ready-made services. But what can you do if the finished services do not fit the project? Customizable AI and ML models in the cloud, which you can train with your own...
Nov
12,
2018
Too many ideas, too little data – Overcome the cold start problem
The cold start problem affects both startups as well as established companies. Nonetheless, it also provides a great opportunity to collect new data with your customer’s problem in focus. How do you solve the cold start problem and arrive at a useful data pipeline? We talked to ML Conference speakers...
Nov
9,
2018
“Designing proper data collection today improves the quality of ML outcomes tomorrow”
Machine learning may have all sorts of use cases, but forecasting? In honor of the upcoming ML Conference, we talked to Philipp Beer about how data scientists can utilize ML in statistical forecasting. We talk about the advantages and disadvantages of modern vs. classical methods, how can one decide between...
Oct
29,
2018
Coding deep learning: The absolute minimum an interested developer should know
Deep Learning is all the hype these days, beating another record most every week but writing code for deep learning is not just coding – it really helps if you have a basic understanding of what’s going on beneath. In this session from last year’s ML Conference, Sigrid Keydana offers...