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- On Sunday, June 3, 2018
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Soaring Raw Materials Demand Is Driving Innovation In Automotive And Consumer Electronics Markets
Python is one of the best programming languages out there, with an extensive coverage in scientific computing: computer vision, artificial intelligence, mathematics, astronomy to name a few.
However, this also requires you to know different libraries and tools, including their advantages and disadvantages, to be able to make a sound decision for the systems that you are building.
We do not aim to list all the machine learning libraries available in Python (the Python package index returns 139 results for “machine learning”) but rather the ones that we found useful and well-maintained to the best of our knowledge.
Moreover, although some of modules could be used for various machine learning tasks, we included libraries whose main focus is machine learning.
For example, although Scipy has some clustering algorithms, the main focus of this module is not machine learning but rather in being a comprehensive set of tools for scientific computing.
Another thing worth mentioning is that we also evaluated the library based on how it integrates with other scientific computing libraries because machine learning (either supervised or unsupervised) is part of a data processing system.
If the library that you are using does not fit with your rest of data processing system, then you may find yourself spending a tremendous amount of time to creating intermediate layers between different libraries.
Considering how much time is spent on cleaning and structuring the data, this makes it very convenient to use the library as it tightly integrates to other scientific computing packages.
Moreover, it has also limited Natural Language Processing feature extraction capabilities as well such as bag of words, tfidf, preprocessing (stop-words, custom preprocessing, analyzer).
It is better than Scikit-learn in some aspects (classification methods, some preprocessing capabilities) as well, but it does not fit well with the rest of the scientific computing ecosystem (Numpy, Scipy, Matplotlib, Pandas) as nicely as Scikit-learn.
Even though deep learning is a subsection Machine Learning, we created a separate section for this field as it has received tremendous attention recently with various acqui-hires by Google and Facebook.
Theano is the most mature of deep learning library. It provides nice data structures (tensors) to represent layers of neural networks and they are efficient in terms of linear algebra similar to Numpy arrays.
There is another library built on top of Theano, called PyLearn2 which brings modularity and configurability to Theano where you could create your neural network through different configuration files so that it would be easier to experiment different parameters.
Decaf is a recently released deep learning library from UC Berkeley which has state of art neural network implementations which are tested on the Imagenet classification competition.
You could determine the properties of your neural networks through YAML files(similar to Pylearn2) which provides a nice way to separate your neural network from the code and quickly run your models.
Following packages for respective programming languages could be used to combine Python with other programming languages: These are the libraries that did not release any updates for more than one year, we are listing them because some may find it useful, but it is unlikely that these libraries will be maintained for bug fixes and especially enhancements in the future: If we are missing one of your favorite packages in Python for machine learning, feel free to let us know in the comments.
- On Thursday, March 21, 2019
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