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.
ML.NET9.6 7.2 Synapses VS ML.NETML.NET is an open source and cross-platform machine learning framework for .NET.
Accord.NET9.5 6.7 L2 Synapses VS Accord.NETMachine learning framework combined with audio and image processing libraries (computer vision, computer audition, signal processing and statistics).
TensorFlow.NET8.3 2.1 Synapses VS TensorFlow.NET.NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
AForge.NET7.1 0.0 L2 Synapses VS AForge.NETAForge.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.
F# Data6.3 6.1 Synapses VS F# DataF# Data: Library for Data Access
Deedle6.3 3.7 Synapses VS DeedleEasy to use .NET library for data and time series manipulation and for scientific programming
Accord.NET Extensions6.1 0.0 L5 Synapses VS Accord.NET ExtensionsAdvanced image processing and computer vision algorithms made as fluent extensions.
encog-dotnet-core5.5 0.0 Synapses VS encog-dotnet-coreEncog Machine Learning Framework
numl5.3 0.0 L3 Synapses VS numlMachine Learning for .NET
Catalyst5.0 5.6 Synapses VS 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.
Spreads4.6 3.7 Synapses VS SpreadsSeries and Panels for Real-time and Exploratory Analysis of Data Streams
R Provider4.3 0.0 Synapses VS R ProviderAccess R packages from F#
Infer.NET1.8 0.0 Synapses VS Infer.NETUAI 2015. Kernel-based just-in-time learning for expectation propagation
FsLab1.4 0.0 Synapses VS FsLabFsLab project templates - download as ZIP to get started!
SciSharp STACKA rich machine learning ecosystem for .NET created by porting the most popular Python libraries to C#.
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They vary from L1 to L5 with "L5" being the highest.
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A lightweight library for neural networks that runs anywhere!
- Add one dependency to your project.
- Write a single import statement.
- Use a few pure functions.
You are all set!
It runs anywhere
It's compatible across languages
- The interface is common across languages.
It offers visualizations
Get an overview of a neural network by taking a brief look at its svg drawing.
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.
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.