Deep down ML is a pure numbers game. With very few exceptions, the actual input to an ML Model is always a collection of float values. We talked with Christoph Henkelmann about the way ML algorithms work on words and letters, the difference between image and text and how to handle textual input properly.
What is the driver behind the growing interest in using Kubernetes for data science and machine learning applications? Terry Shea of Kublr explains why you shouldn’t avoid Kubernetes if you have ML and data science projects.
Google released the beta version of Android P, the company’s latest mobile operating system, and it’s stuffed with all kinds of smartness! Machine learning enthusiasts, gather round; it’s play time!
Humanity is confronted more than ever with artificial intelligence (AI), yet it is still challenging to find a common ground. We talked with Marisa Tschopp, researcher at scip ag about Artificial Intelligent Quotient (A-IQ), how to automate A-IQ testing and more.
If you want to know the ABCs of machine learning or if you need to brush up on basic concepts, the first part of the latest JAX Magazine issue is for you. If you’re a machine learning aficionado or expert, you’ll surely enjoy the second part.
Deep learning is probably one of the hottest topics in the field of software development at the moment. We spoke with Shirin Glander and Uwe Friedrichsen, both giving an introduction into deep learning at JAX 2018, about such future prospects.
No experience? No problem! NML 2.0 empowers developers with no AI experience to build their own ML models
Who says you need that data science degree? NML 2.0 and the AI Studio 2.0 empower developers with no AI experience to build their own machine learning models.
It is no news that Python is one of the most popular languages out there and one of the reasons for this success is that it offers an extensive coverage for scientific computing. Here we take a closer look at the top 10 Python tools for machine learning and data science.
Stop sharing machine learning models like it’s the stone age. Comet.ml intends to become the GitHub of ML, offering data scientists a simple, easy to use tool to share, compare, and optimize their machine learning models.
Apache Spark 2.3 was released earlier this year; it marked a major milestone for Structured Streaming but there are a lot of other interesting features that deserve your attention. We talked with Reynold Xin, co-founder and Chief Architect at Databricks about the Databricks Runtime and other enhancements introduced in Apache Spark 2.3.
Google’s machine learning framework TensorFlow is on the rise. We spoke to Christoph Henkelmann at ML Conference 2017 about its benefits in the enterprise and the reasons for using Java in this context. Furthermore, we talked about new trends in the world of machine learning.
TensorFlow 1.7 has just arrived. We take a look at one of the cool new features in the latest release: full integration for TensorRT! What does that mean for our favorite machine learning project? Faster performances, for one thing.
How can you design artificial intelligence to benefit society? How do you integrate smart systems into business life? Prof. Wolfgang Henseler addressed these questions in his keynote at the ML Conference 2017, “It’s all about machines and creating their minds”.