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Bender
Bender

4
10
+ 1
0
DMTK
DMTK

3
15
+ 1
0
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Bender vs DMTK: What are the differences?

Bender: A Deep Learning framework for iOS. Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood; DMTK: Microsoft Distributed Machine Learning Tookit. DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces.

Bender and DMTK can be primarily classified as "Machine Learning" tools.

Some of the features offered by Bender are:

  • Neural networks
  • deep learning
  • metal

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

  • DMTK Framework: a flexible framework that supports unified interface for data parallelization, hybrid data structure for big model storage, model scheduling for big model training, and automatic pipelining for high training efficiency.
  • LightLDA, an extremely fast and scalable topic model algorithm, with a O(1) Gibbs sampler and an efficient distributed implementation.
  • Distributed (Multisense) Word Embedding, a distributed version of (multi-sense) word embedding algorithm.

Bender and DMTK are both open source tools. DMTK with 2.69K GitHub stars and 595 forks on GitHub appears to be more popular than Bender with 1.65K GitHub stars and 87 GitHub forks.

What is Bender?

Easily craft fast Neural Networks on iOS! Use TensorFlow models. Metal under the hood.

What is DMTK?

DMTK provides a parameter server based framework for training machine learning models on big data with numbers of machines. It is currently a standard C++ library and provides a series of friendly programming interfaces.
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              What are some alternatives to Bender and DMTK?
              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.
              scikit-learn
              scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
              Keras
              Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
              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.
              ML Kit
              ML Kit brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-use package.
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