Leaderboard

We have noticed in the last few years that the outstanding support from NI technical support quickly detorated to the level of standard untrained technical support that call centers located in some low income countries often provide. However I have to say that this trend seems to have been reversed in recent times. I had three technical support questions in the course of about one year now, non was standard and included things that were simply not possible because the feature was officially not present. The support people were very helpful in trying to find workarounds and in two cases provided even solutions that were based on information that was gained directly from the product owners and developers to access the feature through direct behind the scene configuration files and APIs. In both cases with the strong warning that this was not the officially sanctioned way to do things and that there was a real chance that it may break in future versions, but that it was at the moment the best that could be done.

TDF team is proud to propose for free download the scikit-learn library adapted for LabVIEW in open source. LabVIEW developer can now use our library for free as simple and efficient tools for predictive data analysis, accessible to everybody, and reusable in various contexts. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy from the famous scikit-learn Python library. Coming soon, our team is working on the « HAIBAL Project », deep learning library written in native LabVIEW, full compatible CUDA and NI FPGA. But why deprive ourselves of the power of ALL the FPGA boards ? No reason, that's why we are working on our own compilator to make HAIBAL full compatible with all Xilinx and Intel Altera FPGA boards. HAIBAL will propose more than 100 different layers, 22 initialisators, 15 activation type, 7 optimizors, 17 looses. As we like AI Facebook and Google products, we will of course make HAIBAL natively full compatible with PyTorch and Keras. Sources are available now on our GitHub for free : https://www.technologies-france.com/?page_id=487

One thing to keep in mind: LabVIEW and Python are high-level languages, while C and C++ are low-level languages. High-level languages are easier to get started with, and they provide some protection against common errors. For example, if you use an invalid reference in LabVIEW, you get a helpful error messsage telling you where the issue is; if you use an invalid pointer in C/C++, you could get silent memory corruption. Low-level languages can be more powerful and efficient if you know how to use them properly. However, it does take a lot longer to learn them properly. Whichever path you choose, the guidance of an good experienced teacher can get you further and faster then online tutorials.