Machine learning

Top 20 OpenSource Neural networks projects

Are you looking for an open source neural network project? In this article you will find those projects  and links to access the code in GitHub. Let’s take a tour of the top 20 open source neural network projects. 

1. TensorFlow

TensorFlow is an open-source project used in machine learning. It contains a complete, adjustable environs of libraries, tool and community assets that allow analyzers push the ultra-modern ML, and it becomes easy for constructors to build and install ML-driven applications. It supplies stable C++ and Python API and also uncollateralized backward well-suited API used with other languages.

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2. PyTorch

 PyTorch  is a python collection that gives 2 high level characteristics 

  • Tensor computation that has a rigid GPU acceleration.
  • Rooted neural networks constructed on tape-based autograd scheme.

You can utilize your treasured Python packages like SciPy, Cython, and NumPy to lengthen PyTorch when required.

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3. Accord.NET

 Accord.NET project gives machine learning, artificial intelligence, image processing, statistics and computer vision techniques to .NET. You can use it on Microsoft Windows, Unity3D, Xamarin, mobile, or Windows store apps. Accord.NET supports logistic regression, vector machines, neural networks, decision trees, and deep learning.  After amalgamation with Project AForge.NET, it now provides a unified API uses in training/learning machine learning framework that is extensible and easy to use.

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4. PyCNN

Cellular Neural Networks are an analogous computing paradigm they are the same with neural networks with only one difference that information exchange is restricted to neighboring units.  CNN processors are utilized in image processing. They were modelled to carry out image processing I.e., instantaneous ultra-high-frame-rate (10,00 frames) which could not be achieved by the digital processors.

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5. Encog-dotnet-core

 Encog is a machine learning structure that supports pure C#/Java. Its neural network features were well known, and it was utilized by many persons. The fact that it’s created with pure Java, makes it easy to remodel and use it when you are creating a neural network from scratch.  It supports advanced some algorithms and also does aid classes to standardize and process figures.

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6. GluonCV

 GluonCV gives fulfillment of futuristic deep learning framework in computer perception. It is constructed for students, researchers and engineers for speedy research ideas and prototype products established on those designs. Its toolkit contains the following features 

  • Supports MXNet and PyTorch
  • Community supports
  • Well-designed APIs that minimize implementation complexity.

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7. Gorgonia

 Gorgonia is a collection that facilitates machine learning in Go. It is similar to Tensor-flow or Theano, and this library is a low-level library just like Theano while it has more great goals like TensorFlow. Features

  • It carries out automatic differentiation
  • It carries out symbolic differentiation
  • It can carry out numerical stabilization
  • It is fast
  • Supports GPGPU/CUDA computation.
  • It advocates for distributed computing.

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8. Seq2seq

 Seq2seq is a universal encoder-decoder structure for TensorFlow that is used for text summarization, machine translation, image captioning and conversational modeling.  It was designed to fulfill the following goals: 

  • General Purpose- I was built to do machine translation but now handles other activities like conversational modelling and more
  • It was built to be highly usable by supporting different types of input data
  • It was built to be extensible

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9. Brain.JS

 Brain.js is a collection of Neural networks that are coded using JavaScript. It’s a GPU speeding up neural connections in JavaScript for Node.js and Browsers. It is a fantastic choice to construct a neural network. It studies relationship and patterns between what’s getting in and out for it to make a decision when it encounters a similar issue. CLICK FOR MORE DETAILS

10. Chainer

 Chainer it’s a deep learning structure which is Python-based, and it aims at being flexible. It has unmanned differentiation APIs according to dynamic computation graphs. It also has object-oriented top tier APIs for constructing and training neural networks. CLICK FOR MORE DETAILS

11. BigDL

 BigDL is a scattered rooted learning collection for Apache Spark. It allows you to code rooted learning apps like normal Spark programs, and it has the ability to run over existing Hadoop or Spark clusters. Features 

  • It contains Plentiful rooted learning support.
  • It has Extra high performance.
  • It has a well-organized scale out.

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12. ONNX

  Open Neural Network Exchange is an unlocked environ that empowers Artificial Intelligence you to select the proper tools as your project evolves. It gives an open-source design for AI designs, traditional ML and deep learning. It defines normal data types, inbuilt operators and extendable computation graph model.  It is supported widely, and it’s found on different tools, hardware and frameworks this enhances compatibility between non-identical frameworks.

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13. SpaCY

 SpaCY is a collection for modern natural language processing in Cython and Python. It is designed using real world products and constructed on modern research. It has inbuilt pretrained pipelines, training for over 60 languages and supports tokenization. It has futuristic neural network models and speed for parsing, tagging, multitask learning, text classification and named entity recognition. CLICK FOR MORE DETAILS

14. TFLearn

  TFLearn is a transparent and modular rooted learning collection constructed above TensorFlow. It gives a higher-level API to TensorFlow for it to help accelerate experimentation Features:

  • It is easy to use and deals with top tier API to help in building of rooted neural networks that has examples and tutorials.
  • Fast duplication through top tier modular inbuilt regularizes, metrics, optimizers and network layers.

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15. ML5

 ML5 is a social machine learning used for web. Its goal is to make machine learning welcoming for a large audience of creative coders, students and artists. It gives entry to machine learning models and algorithms in the browser constructed above TensorFlow.js.

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16. Genann

 Genann is a negligible wee-testes collection used to train and feed forwarding unnatural neural networks in C. Features

  • Simple, thread-safe and fast
  • Can be easily extended
  • Accommodated in a single header file and source code
  • Includes test suite and examples
  • It is free for almost all uses since its released subject to zlib license
  • It is compatible with other training methods i.e., genetic algorithms, classic optimization etc.

17. FANN(Fast Artificial Neural Networks)

 FANN a free neural network collection that performs layered artificial neural networks in C and supports scant and fully connected networks. It binds to over 15 programming languages and has a couple of graphical user interfaces. Features

  • Back propagation training
  • It is well documented
  • Its execution time is 150 times faster than other collections.
  • It is versatile and easy to use.
  • Cache optimized
  • Can use fixed- and floating-point numbers

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18. Tiny-dnn

  Tiny-dnn is a C++ 14 administration of deep learning. It is good for rooted learning on restricted embedded systems, IoT services and computational resource. Features

  • It is simple to implement and integrate it with actual applications
  • It is highly portable i.e., it can execute anywhere as long as that compiler supports C++14.
  • It’s reasonably fast.

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19. NiftyNet

 NiftyNet  is a Tensorlow established open-source complex neural networks programme  for researching in image-guided therapy and medical image investigation. Its modular constitution is designed for dividing networks and pretrained models. Features

  • Easily customizable interfaces of network elements
  • Support for 2.5-D, 3-D, 4-d inputs.

20. CLgen

 CLgen is a free application for coming up with executable programs using rooted (deep) learning. It learns to program by utilizing neural networks that design the semantics and utilization from huge volumes of program bits.  It produces numerous-core OpenCL programs that represent the learned program, but it is different from it

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Conclusion

The above are the top best neural network projects that you can access on GitHub to do deep learning. What you need to do is just choose one that suits your needs. 

karani Jackson

I am a dedicated, fast, and a time conscious writer. I love doing research and learning new things. I am an upcoming youtuber

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karani Jackson

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