5
May
/
Explainability – a promising next step in scientific machine learning
With the emergence of deep neural networks, the question has arisen how machine learning models can be not only accurate but also explainable. In this article, you will learn more about explainability and what elements it consists of, and why we need expert knowledge to interpret machine learning results to avoid making the right decisions for the wrong reasons.
28
Jan
/
Deep Learning: Not only in Python
Although there are powerful and comprehensive machine learning solutions for the JVM with frameworks such as DL4J, it may be necessary to use TensorFlow in practice. This can, for example, be the case if a certain algorithm exists only in a TensorFlow implementation and the effort to port the algorithm into another framework is too high. Although you interact with TensorFlow via a Python API, the underlying engine is written in C++. Using the TensorFlow Java wrapper library, you can train and inference TensorFlow models from the JVM without having to rely on Python. Existing interfaces, data sources, and infrastructures can be integrated with TensorFlow without leaving the JVM.
6
May
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 learning that promises deep-reaching learning success without human intervention and is oriented towards the function and operation of neural networks in the human brain.