deeplearn-rs Alternatives & Competitors

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

Open Source

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

Connect to 200+ Wearables & Health Data Sources with One API.

Key Sahha features:

Health & Wearable Integrations Health Scores Archetypes Insights

Try for free paid Free Trial $199.0 / Monthly (Full access)

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

Open Source

Heroku for deep learning.

Key Floyd features:

Ease of Use Collaboration Managed Infrastructure Resource Scalability

Social media analytics platform.

Key NetBase features:

Comprehensive Data Integration Advanced AI and NLP Customizable Dashboards Industry-Specific Solutions

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.

Open Source

Sponsors. Use Mocha at Work? Ask your manager or marketing team if they'd help support our project. Your company's logo will also be displayed on npmjs. com and our GitHub repository.

Key Mocha features:

Advanced Tracking Capabilities Comprehensive Toolset Cross-platform Compatibility Time-saving Automation

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

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

Caffe is an open source, deep learning framework.

Key Caffe features:

Performance Modularity Pre-trained Models Community Support

Open Source

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

BigDL is a distributed deep learning library for Apache Spark.

Deep Scalable Sparse Tensor Network Engine (DSSTNE) is a library for building Deep Learning (DL) and machine learning (ML) models.

Key Amazon DSSTNE features:

Scalability Distributed Processing Efficient for Sparse Data Flexibility in Model Architecture