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SigOpt VS Pylearn2

Compare SigOpt VS Pylearn2 and see what are their differences

LibHunt
LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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SigOpt logo SigOpt

Optimize Everything. Tune your experiments automatically to get better results, faster. A/B testing.

Pylearn2 logo Pylearn2

Pylearn2 is a library for machine learning research.
  • SigOpt Landing page
    Landing page //
    2023-04-09
  • Pylearn2 Landing page
    Landing page //
    2023-09-15

Category Popularity

0-100% (relative to SigOpt and Pylearn2)

SigOpt

Pylearn2

Data Science And Machine Learning
69 69%
Data Science And Machine Learning
31% 31
Python Tools
69 69%
Python Tools
31% 31
Data Science Tools
69 69%
Data Science Tools
31% 31
Software Libraries
50 50%
Software Libraries
50% 50

User comments

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Social recommendations and mentions

Based on our record, Pylearn2 seems to be more popular. It has been mentiond 1 time since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

SigOpt mentions (0)

We have not tracked any mentions of SigOpt yet. Tracking of SigOpt recommendations started around Mar 2021.

Pylearn2 mentions (1)

  • iNeural : Update (8.12.21)
    It is developed by taking inspiration from libraries such as iNeural, FANN, pylearn2, EBLearn, Torch7. Written mostly in C++, iNeural also leverages the power of Python. The biggest reason for its development is that it needs very few dependencies. For this reason, it is expected to be suitable for working in systems with limited system requirements. - Source: dev.to / over 2 years ago

What are some alternatives?

When comparing SigOpt and Pylearn2, you can also consider the following products

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

NumPy - NumPy is the fundamental package for scientific computing with Python

OpenCV - OpenCV is the world's biggest computer vision library

Dataiku - Dataiku is the developer of DSS, the integrated development platform for data professionals to turn raw data into predictions.

Exploratory - Exploratory enables users to understand data by transforming, visualizing, and applying advanced statistics and machine learning algorithms.

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