Amazon DSSTNE
Deep Scalable Sparse Tensor Network Engine (DSSTNE) is a library for building Deep Learning (DL) and machine learning (ML) models.
Amazon DSSTNE Alternatives & Competitors
The best Amazon DSSTNE alternatives based on verified products, community votes, reviews and other factors.
Some of the top features or benefits of Amazon DSSTNE are: Scalability, Distributed Processing, Efficient for Sparse Data, and Flexibility in Model Architecture. You can visit the info page to learn more.
We have listed more than 10 alternatives to Amazon DSSTNE. You can find them below. The top competitors are: TensorFlow, Deeplearning4j, and Keras. Apart from the top ones, people also compare Amazon DSSTNE with NVIDIA DIGITS, Caffe, and Scikit-learn.
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/tensorflow-alternatives
TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.
Key TensorFlow features:
Comprehensive Ecosystem Community and Support Flexibility Integrations
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/deeplearning4j-alternatives
Deeplearning4j is an open-source, distributed deep-learning library written for Java and Scala.
Key Deeplearning4j features:
Java Integration Scalability Commercial Support Compatibility with Hardware
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Key KeptPDF features:
Simple PDF Editing Web-Based Accessibility Multiple File Format Support No Installation Required
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/keras-alternatives
Keras is a minimalist, modular neural networks library, written in Python and capable of running on top of either TensorFlow or Theano.
Key Keras features:
User-Friendly Modularity Pre-trained Models Integration with TensorFlow
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/floyd-alternatives
Heroku for deep learning.
Key Floyd features:
Ease of Use Collaboration Managed Infrastructure Resource Scalability
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/deepdetect-alternatives
DeepDetect is a deep learning API and server that is written in C++11 to makes deep learning easy to work with and integrate into existing applications.
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/theano-alternatives
Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features: tight integration with NumPy – Use numpy.
Key Theano features:
Symbolic Differentiation Optimized GPU Computation Extensibility Mathematical Expression Optimization
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/knet-alternatives
Knet is a deep learning framework that supports GPU operation and automatic differentiation using dynamic computational graphs for models.
Key Knet features:
Efficiency Flexibility Julia Integration Community and Support
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/caffe-alternatives
Caffe is an open source, deep learning framework.
Key Caffe features:
Performance Modularity Pre-trained Models Community Support
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/deeppy-alternatives
DeepPy is a MIT licensed deep learning framework that tries to add a touch of zen to deep learning as it allows for Pythonic programming.
Key DeepPy features:
Ease of Use Python Integration Lightweight
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/bigdl-alternatives
BigDL is a distributed deep learning library for Apache Spark.
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/nvidia-digits-alternatives
DIGITS (the Deep Learning GPU Training System) is a webapp for training deep learning models.
Key NVIDIA DIGITS features:
User-Friendly Interface Pre-trained Models Visualization Tools Multi-GPU Support
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/tflearn-alternatives
TFlearn is a modular and transparent deep learning library built on top of Tensorflow.
Key TFlearn features:
User-Friendly Interface Modular Design Integration with TensorFlow Pre-built Models
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/paddlepaddle-alternatives
PaddlePaddle is an open source Deep Learning Platform for Enterprise and Research.
