MXNet vs Yellowbrick

MXNet vs Yellowbrick: What are the differences?

Developers describe MXNet as "A flexible and efficient library for deep learning". A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. On the other hand, Yellowbrick is detailed as "Visual analysis and diagnostic tools to facilitate machine learning model selection". It is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, it combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow.

MXNet and Yellowbrick can be primarily classified as "Machine Learning" tools.

Some of the features offered by MXNet are:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

On the other hand, Yellowbrick provides the following key features:

• Evaluate the stability and predictive value of machine learning models and improve the speed of the experimental workflow
• Provide visual tools for monitoring model performance in real-world applications
• Provide visual interpretation of the behavior of the model in high dimensional feature space.

MXNet is an open source tool with 18.6K GitHub stars and 6.63K GitHub forks. Here's a link to MXNet's open source repository on GitHub.