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Torch AI VS Pylearn2

Compare Torch AI 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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Torch AI logo Torch AI

Torch is a scientific computing framework for LuaJIT.

Pylearn2 logo Pylearn2

Pylearn2 is a library for machine learning research.
  • Torch AI Landing page
    Landing page //
    2019-09-21
  • Pylearn2 Landing page
    Landing page //
    2023-09-15

Category Popularity

0-100% (relative to Torch AI and Pylearn2)

Torch AI

Pylearn2

Data Science And Machine Learning
24 24%
Data Science And Machine Learning
76% 76
Data Science Tools
19 19%
Data Science Tools
81% 81
Python Tools
19 19%
Python Tools
81% 81
Software Libraries
50 50%
Software Libraries
50% 50

User comments

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Social recommendations and mentions

Based on our record, Torch AI should be more popular than Pylearn2. It has been mentiond 3 times 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.

Torch AI mentions (3)

  • About luarocks, Lua ecosystem, etc.
    Here is the url to Torch: http://torch.ch/. Source: about 1 year ago
  • #
    I think you can use torch7 to do data science things but its development is a bit halted or something like that. It was pretty cool tho, you would have a C-level fast interpreter (LuaJIT) using a nice library. Source: about 2 years ago
  • 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

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 Torch AI and Pylearn2, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

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

Caffe - Caffe is an open source, deep learning framework.

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

Learn X in Y minutes - LearnXinYminutes isn’t a good way to learn your first programming language, but it’s a great way to...

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

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