Software Alternatives, Accelerators & Startups

Neuroph VS Spry Simulation

Compare Neuroph VS Spry Simulation and see what are their differences

Neuroph logo Neuroph

Neuroph is lightweight Java neural network framework to develop common neural network architectures.

Spry Simulation logo Spry Simulation

Microsoft Excel add-in which enables system dynamics, discrete-event and Monte Carlo simulation in modern spreadsheets, especially with small to medium-sized simulation models and for a proof-of-concept tasks.
  • Neuroph Landing page
    Landing page //
    2019-11-24
  • Spry Simulation Landing page
    Landing page //
    2023-01-06

Neuroph features and specs

No features have been listed yet.

Spry Simulation features and specs

  • System dynamics
  • Discrete events modeling
  • Monte Carlo simulation
  • Simulation elements as spreadsheet functions
  • Charts
  • Optimization

Neuroph videos

Neuroph Hacking Session at JCrete

Spry Simulation videos

No Spry Simulation videos yet. You could help us improve this page by suggesting one.

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Category Popularity

0-100% (relative to Neuroph and Spry Simulation)
Spreadsheets
57 57%
43% 43
Simulation
54 54%
46% 46
Simulation Modeling
43 43%
57% 57
Data Science And Machine Learning

User comments

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What are some alternatives?

When comparing Neuroph and Spry Simulation, you can also consider the following products

Netron - Open-source visualizer for neural network, deep learning and machine learning models.

GoldSim - GoldSim is the premier Monte Carlo simulation software solution for dynamically modeling complex systems in business, engineering and science.

Deep playground - Deep playground is an interactive visualization of neural networks, written in typescript using d3.

Alkanet - Visual builder for neural nets, drag and drop for deep learning.

Neural Designer - Neural Designer is a high performance data science and machine learning platform.

AnyLogic - AnyLogic has changed simulation modeling and expanded its application into complex business environments. The unmatched flexibility of multimethod modeling allows users to capture the complexity of virtually any system, at any level of detail.