I am trying to test if my GAN implemented by TensorFlow 2.0 can reproduce the results in the papers. However, all the available evaluation metrics are based on TensorFlow 1.X or Pytorch. And some operations like placeholder, session is incompatible in 2.0 version. Could anyone help, please?
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I went through the manuals available on the IBM website. However, there is no explanation on how to annotate a model for R API users. Does anyone have a hint?
We are setting up Azure ML SDK in Python. When we are creating models using Azure ML SDK in Python notebook We have to manually write the code to use feature of Azure ML and Scikit learn-- But if we model in ML studio we will do all that easily by drag and drop. Required solution is Can we build a model in Azure ML Studio and use the model in Azure ML SDK (Python Notebook). No more manual coding for Model creation will be involved. Please suggest.
#Ram mentions going from UI to Python SDK, but I recommend investigating the opposite direction with ModuleSteps in the SDK. If you can package your code into Modules, they can be called with Modules, which are on the roadmap to be made available within the visual designer.
Yes currently it's in road map to export an Azure ML Visual Designer flow into a python code. Use a graphical user interface in order to generate automatically the python code will be available in near future.
I use R, but lately are trying to implement Model Predictive Control as applied in Control Engineering on my models, but it seems that this area in R is non-existent as compared to Matlab where it is quite easy to do System Identification and create transfer functions which can be deployed inside the Model Predictive Control module. Anybody knows where to look, or which packages to use for MPC and transfer functions in R to build MPC models?
There are a growing number of Python packages for control engineering. One option is to use the reticulate R package to interface to Python functions. Here are some Control Engineering packages in Python:
SciPy.signal for signal processing and system modeling
SymPy for Laplace transforms and differential equation analytic solutions
Control Systems Library
Chemical Process Control from Jeff Kantor, Notre Dame
Process Dynamics and Control in Python at BYU (my course)
Advanced Control and Machine Learning in Python at BYU (also my course)
Here is an example of running MPC in Python on the Temperature Control Lab.
I'm trying to use the google-analytics framework to create predictive analysis tools. For example I would like to cluster my webpage visitors, etc.
In general, is there any list of machine learning algorithms implemented by this framework? for example: regression, clustering, classification, feature selection, etc.
Thank you for any help
Depending upon your language of choice, you might want to export your Google Analytics Metrics to flat files or a database and then start experimenting with ML models. Two popular languages with stable ML Implementations are Python and R. R's caret package includes tools for building a predictive model pipeline. Python's scikit-learn also contains implementations of all major classes of ML algorithms.
When you say GA framework I'll assume you're referring to the set of Google Analytics APIs listed here. The framework by itself doesn't provide machine learning capabilities. It merely provides access to the processed and aggregated GA data stored in Google's servers. You can use the API and feed the data to a machine learning application/system/program that does all of the stuff you mentioned.
I am wondering if anyone has faced this issue before. I use the package lmerTest to run mixed-effects models in R because it has a handy way of providing p-values. This package by default loads the most current version of the lme4 package. However, the lme4 current version has some issues and it sometimes doesn't converge, so the lme4 developers have made available a new package (named lme4.0), which is a bugfix-only version of the old pre-1.0 lme4. This works great, and the models usually converge, so that is what I use to analyze my data.
I would like to have lmerTest but have it load lme4.0 instead of the current version of lme4.Does anyone know how to achieve this?
Thanks for your help!
This isn't really feasible without serious hacking: essentially, take an older version of lmerTest, download the source, hack it to look for lme4.0 rather than lme4, and install locally. Or download (from the CRAN archives) and install older versions of lme4 and lmerTest (and pbkrtest: maintaining an archaic setup will get progressively more difficult, and you will have to backport or forego bug fixes as they appear in newer versions).
Since many of the problems with new lme4 have been cleaned up with the switch in default optimizers from Nelder-Mead to BOBYQA, my advice would be to run a range of comparisons between lme4.0 and lme4, convince yourself that there are no problems (and send information about persistent problems to the lme4 maintainers, who would greatly appreciate it!), and move on to the new version.