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Caffe

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Xcessiv

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Xcessiv vs Caffe: What are the differences?

Developers describe Xcessiv as "Fully managed web application for automated machine learning". A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python. On the other hand, Caffe is detailed as "A deep learning framework". It is a deep learning framework made with expression, speed, and modularity in mind.

Xcessiv and Caffe can be primarily classified as "Machine Learning" tools.

Some of the features offered by Xcessiv are:

  • Fully define your data source, cross-validation process, relevant metrics, and base learners with Python code
  • Any model following the Scikit-learn API can be used as a base learner
  • Task queue based architecture lets you take full advantage of multiple cores and embarrassingly parallel hyperparameter searches

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

  • Extensible code
  • Speed
  • Community

Xcessiv and Caffe are both open source tools. Caffe with 29.2K GitHub stars and 17.6K forks on GitHub appears to be more popular than Xcessiv with 1.2K GitHub stars and 101 GitHub forks.

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What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

What is Xcessiv?

A web-based application for quick, scalable, and automated hyperparameter tuning and stacked ensembling in Python.

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    What are some alternatives to Caffe and Xcessiv?
    TensorFlow
    TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
    Torch
    It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
    PyTorch
    PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
    Caffe2
    Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
    Keras
    Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
    See all alternatives