Tulip Indicators VS frugally-deep

Compare Tulip Indicators vs frugally-deep and see what are their differences.

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Tulip Indicators frugally-deep
4 5
802 1,045
0.7% -
5.2 8.0
3 months ago 13 days ago
C C++
GNU Lesser General Public License v3.0 only MIT License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

Tulip Indicators

Posts with mentions or reviews of Tulip Indicators. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-12-23.

frugally-deep

Posts with mentions or reviews of frugally-deep. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-03.

What are some alternatives?

When comparing Tulip Indicators and frugally-deep you can also consider the following projects:

Tulip Cell - TulipCell is an Excel add-in providing 100+ technical analysis indicators.

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

btsk - Behavior Tree Starter Kit

tensorflow - An Open Source Machine Learning Framework for Everyone

CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit

tiny-cnn - header only, dependency-free deep learning framework in C++14

ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library

Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System

AI-Toolbox - A C++ framework for MDPs and POMDPs with Python bindings

Genann - simple neural network library in ANSI C

Deeplearning4j - Suite of tools for deploying and training deep learning models using the JVM. Highlights include model import for keras, tensorflow, and onnx/pytorch, a modular and tiny c++ library for running math code and a java based math library on top of the core c++ library. Also includes samediff: a pytorch/tensorflow like library for running deep learning using automatic differentiation.

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