Based on our record, Scikit-learn seems to be a lot more popular than CherryPy. While we know about 27 links to Scikit-learn, we've tracked only 2 mentions of CherryPy. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Generally, what needs to be done to create an Django/Electron app is to package (I'm using pyInstaller)the Django app into an stand-alone executable and then bundle that into an Electron app. The question is which server should be used for this case to server Django before packaging it with pyInstaller? At the moment I'm using cherryPy as a WSGI web server to serve Django. Source: about 2 years ago
I know there are plenty of questions about Flask and CherryPy and static files but I still can't seem to get this working. Source: about 2 years ago
Firstly, we need a connection to Memgraph so we can get edges, split them into two parts (train set and test set). For edge splitting, we will use scikit-learn. In order to make a connection towards Memgraph, we will use gqlalchemy. - Source: dev.to / 11 months ago
The ML component is based on scikit-learn which differentiates it from purely list-based filters. It couples this with a full-featured wireless router (RaspAP) in a single device, so it fulfills the needs of a use case not entirely addressed by Pi-hole. Source: 12 months ago
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive... Source: 12 months ago
Scikit-learn is a machine learning library that comes with a number of pre-built machine learning models, which can then be used as python wrappers. Source: about 1 year ago
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning... - Source: dev.to / about 1 year ago
Django - The Web framework for perfectionists with deadlines
Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
Flask - a microframework for Python based on Werkzeug, Jinja 2 and good intentions.
OpenCV - OpenCV is the world's biggest computer vision library
web2py - Web2py is an open source web application framework.
NumPy - NumPy is the fundamental package for scientific computing with Python