Synapses alternatives and similar packages
Based on the "Machine Learning and Data Science" category.
Alternatively, view Synapses alternatives based on common mentions on social networks and blogs.
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ML.NET
ML.NET is an open source and cross-platform machine learning framework for .NET. -
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TensorFlow.NET
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AForge.NET
AForge.NET Framework is a C# framework designed for developers and researchers in the fields of Computer Vision and Artificial Intelligence - image processing, neural networks, genetic algorithms, machine learning, robotics, etc. -
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Deedle
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Accord.NET Extensions
Advanced image processing and computer vision algorithms made as fluent extensions. -
Catalyst
🚀 Catalyst is a C# Natural Language Processing library built for speed. Inspired by spaCy's design, it brings pre-trained models, out-of-the box support for training word and document embeddings, and flexible entity recognition models. -
Spreads
Series and Panels for Real-time and Exploratory Analysis of Data Streams -
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Infer.NET
UAI 2015. Kernel-based just-in-time learning for expectation propagation -
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SciSharp STACK
A rich machine learning ecosystem for .NET created by porting the most popular Python libraries to C#.
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* Code Quality Rankings and insights are calculated and provided by Lumnify.
They vary from L1 to L5 with "L5" being the highest.
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README
Synapses
A lightweight library for neural networks that runs anywhere!
Getting Started
Why Sypapses?
It's easy
- Add one dependency to your project.
- Write a single import statement.
- Use a few pure functions.
You are all set!
It runs anywhere
Supported languages:
It's compatible across languages
- The interface is common across languages.
- You can transfer a network from one platform to another via its json instance. Create a neural network in Python, train it in Java and get its predictions in JavaScript!
It offers visualizations
Get an overview of a neural network by taking a brief look at its svg drawing.
It's customizable
You can specify the activation function and the weight distribution for the neurons of each layer. If this is not enough, edit the json instance of a network to be exactly what you have in mind.
It's efficient
The implementation is based on lazy list. The information flows smoothly. Everything is obtained at a single pass.
Data preprocessing is simple
By annotating the discrete and continuous attributes, you can create a preprocessor that encodes and decodes the datapoints.
Works for huge datasets
The functions that process big volumes of data, have an Iterable/Stream as argument. RAM should not get full!
It's well tested
Every function is tested for every language. Please take a look at the test projects.
Dependencies
Misc
