Today 3 articles about #KNIME performance, handling large files and optimal settings
KNIME Snippets (2): Unearthing Hidden Node Gems — Managing Missing Values, Row Numbers and some Quick Java and Paths
KNIME Snippets (1) — Collect and Restore — or how to handle many large files and resume loops
Mastering KNIME: Unlocking Peak Performance with Expert Tips and Smart Settings
#knime is excellent - but there are a few things you might want to know or hints that might help you to boost its performance. And you should also consider things like backup or virus scanners. A blog not for the light stuff but to go behind the scenes. And if your question is not covered: you can always ask on the KNIME forum.
#Python graphics are made easy with #KNIME’s #lowcode approach. From scatter, violin and density plots to PNG files and Excel exports, these examples will help you transform your data into actionable insights. Also, find out what it is about Kernel-Density-Plots and Penguins ...
I do a lot of things with #KNIME but today I explore another #lowcode #DataScience tool called #Orange which is entirely based on #Python and can also do a nice comparison of #machinelearning algorithms. Spoiler alert: the results are mostly the same as with KNIME or 'pure' Python but the tool is fun anyway 🙂
#GitHub repository with additional approach using #vtreat
@dethwench I would encourage you to take a look at the #KNIME platform with the (free) desktop version and the (commercial) KNIME Business Hub (server).
As a platform it 'speaks' SQL (or the big data variants) and you can either process the data in the SQL server or in the KNIME system.
The Best kept Secret in Data Science is KNIME
Why KNIME - my take on the KNIME success story
Integrated Deployment KNIME Blog Articles
Everyone has a #ChatGPT blog these days - so why not do some #analytics tasks with the popular #AI Tool, #KNIME and #Python — add some caution and waffles … (https://medium.com/p/c05709dd3bf5)
@lgatto @RonBeavis @pastelbio @proteomicsnews @neely @wfondrie @metamorpheus @pwilmart @magnuspalmblad @massspec
The files seem to belong to the field of "bioinformatics or computational biology". Since #KNIME has a strong community there and several extensions (https://www.knime.com/bioinformatics-and-next-generation-sequencing-extensions) I assume this is why the .knwf extension is mentioned here. KNWF files in the end are ZIP files that would contain whole KNIME workflow(s) which is a collection of XML files, SVG and sometimes also the data.
Correct. IIRC from #KNIME training, it's the parameters for the workflow, which should allow replication of the original processing.
I've done a bit of snooping,
CPSX appears to be the output of Peptide Shaker https://github.com/compomics/moff-gui
SSV is probably a tabular Separator Separated Value format (https://pypi.org/project/ssv/)
SQT looks like an ancient Bruker format for database search results (https://www.manula.com/manuals/ip2/ip2/1/en/topic/7-9-download-sqt-files)
APL is a MaxQuant peaklist file (https://www.wehi.edu.au/people/andrew-webb/1298/apl-mgf-converter)
HIRING: Data Engineer - Barcelona / Barcelona, Spain https://ai-jobs.net/J43988/ #AI #MachineLearning #DataJobs #MLjobs #bigdata #DataScience #AIjobs #AIcareers #hiringnow #Barcelona #Spain #Architecture #AWS #BigQuery #ComputerScience #DataMining #Dataquality #KNIME #Python #SQL
Machine Learning with #XGBoost or #LightGBM gets better with #hyperparameter optimisation and a tool like #Optuna is there to help. Also, you can integrate the results with #KNIME - a walkthrough with some code (https://medium.com/p/dcf0efdc8ddf)
zu einem Vortrag über #knime habe ich ein paar Anmerkungen verfasst und Beispiele zusammengestellt. Wer an einem Einstieg in die #lowcode Software Plattform interessiert ist kann sich das ansehen
Medium Blog: KNIME Einführung — einige High Lights (deutsch)
Vácuo + #KNIME fechando na cara = tô só o game over do #EarthBound ("It must have all just been a bad dream").
Last call for our #WinterSchool in #DataAnalytics and #MachineLearning (Feb 6-17) at @firstname.lastname@example.org, using #Knime, #RStats, #Python. Registration is open until Jan 31: https://unifr.ch/appecon/en/winterschool/. Plenty of snow in the mountains around #Fribourg, so bring your skis or snowboard!❄️⛷️
Last call for our #WinterSchool in #DataAnalytics and #MachineLearning (Feb 6-17) at @email@example.com, using #Knime, #RStats, #Python. Registration is open until Jan 31: https://unifr.ch/appecon/en/winterschool/. Plenty of snow in the mountains around #Fribourg, so take your skis or snowboards along⛷️
Vamos con una pregunta un poco rara pero que me genera curiosidad: ¿alguien por aquí utiliza, de manera regular en su trabajo, el software #Knime para estadística y ciencia de datos?
No tengo ninguna duda en particular sobre él, es mera curiosidad sobre cómo de extendido está fuera del ambiente académico.
Just 'wrote' my first small snippets of #Python code for a #KNIME user question using the much hyped #ChatGPT (https://openai.com/blog/chatgpt/) and I must say I am somewhat impressed - the initial suggested code was already good but then 'we' started discussing trouble shooting options which were quite on point. Like having a coding-assistant (that one day might take over ....?).
I tried other things, some answers are very general and some links are broken (old training data).
If you are interested in #KNIME and #DeepLearning with #Keras and #TensorFlow and how to set that up with the help of #Python and #Conda you can check out my new story on Medium.
KNIME and Python — Setting up Deep Learning Environments for Keras and TensorFlow
KNIME and Python are just good friends :-) If you want to take it up a notch and get a grip on the whole #Conda, #Yaml and environment stuff and see how to use #Python with #Knime you might want to check out my article on Medium (https://medium.com/@mlxl/knime-and-python-setting-up-and-managing-conda-environments-2ac217792539)
If you are interested in "Advanced #Graphics with #Python and #KNIME " (and also building a so called "Data App") you can watch my talk on YouTube (https://youtu.be/mG2SZiKG9zo?t=2140) and download the full workflow from the hub an use it yourself (https://kni.me/w/nbfX818PlGRUflhK) - #DataScience #DataAnalytics
🔥Join us for our 3rd #WinterSchool in #DataAnalytics and #MachineLearning, Feb 6-17 2023 at @firstname.lastname@example.org @email@example.com (hybrid format)! Courses on #MachineLearning, #DeepLearning, and #CausalAnalysis using #Knime, #RStats, #Python. Register here until Jan 31: https://www.unifr.ch/appecon/en/winterschool/
Happy to have been featured in this (free) book "Best of KNIME - The COTM Collection" as one of many Contributors of the Month (https://www.knime.com/knimepress/COTM). #KNIME has a great community (https://forum.knime.com) and is an outstanding tool for #DataScience and #DataAnalytics 😀
Un predittore di ictus con il mio #etl preferito #knime 🔗 https://www.knime.com/blog/XAI-LIME-stroke-prediction e perché gli Health Consumer-Generated Content #hcgc conteranno sempre di più 🔗 https://link.springer.com/article/10.1007/s41666-022-00114-1
Chissà cosa accadrà quando ci saranno enormi banche dati #fhir
#introduction I am Markus,
I work for as a #DataScientist with experience at telco and insurance firms and as a consultant. Main tool currently is #knime along with #BigData / #SQL systems, #Python and some #rstats. I have used SPSS and SAS extensively in the past.
I am a regular in the KNIME forum (https://forum.knime.com/latest) and was named "Contributor of the Month" :-)
I live in #Cologne and like #Vienna - happen to be Austrian.
I have a degree in #History, Sociology and English (M.A.)
🔥 Registration is open for the 3rd #Fribourg #WinterSchool in #DataAnalytics and #MachineLearning, taking place Feb 6-17 2023 in hybrid format: https://www.unifr.ch/appecon/en/winterschool/ Get trained in quantitative methods for #prediction and #CausalAnalysis using #Knime, #RStats, and #Python 😀
Schmeisse gerade das Rohmaterial zu dem komischen Baerbock-Käsequatsch in die Datenbank. Meine Fingerchen zittern u. ich bin ganz nervös, weil ich sooo gespannt auf die Auswertungen bin.
A mmber of our #oklab analyses the state of #opendata portals in Germany, and developed a processing for the metadata of #ckan portals, aggregated and visualises this in #tableau.
You can have a look here:
And here is a peek into a part of the preprocessing chain necessary, implemented with #knime: