MXNet vs Manifold

MXNet vs Manifold: What are the differences?

MXNet: 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; Manifold: A model-agnostic visual debugging tool for machine learning. Understanding ML model performance and behavior is a non-trivial process, given the intrisic opacity of ML algorithms. Performance summary statistics such as AUC, RMSE, and others are not instructive enough for identifying what went wrong with a model or how to improve it. As a visual analytics tool, Manifold allows ML practitioners to look beyond overall summary metrics to detect which subset of data a model is inaccurately predicting.

MXNet and Manifold belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by MXNet are:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

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

• Performance Comparison View
• Feature Attribution View
• Histogram / heatmap

MXNet and Manifold are both open source tools. It seems that MXNet with 18.3K GitHub stars and 6.52K forks on GitHub has more adoption than Manifold with 778 GitHub stars and 58 GitHub forks.