Keras vs Manifold: What are the differences?
Keras: Deep Learning library for Theano and TensorFlow. Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/; 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.
Keras and Manifold can be categorized as "Machine Learning" tools.
Some of the features offered by Keras are:
- neural networks API
- Allows for easy and fast prototyping
- Convolutional networks support
On the other hand, Manifold provides the following key features:
- Performance Comparison View
- Feature Attribution View
- Histogram / heatmap
Keras and Manifold are both open source tools. It seems that Keras with 46.5K GitHub stars and 17.6K forks on GitHub has more adoption than Manifold with 778 GitHub stars and 58 GitHub forks.
