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Top 6 Opensource AI & Machine Learning Frameworks & Libraries

Naveen Jindal| AI | 7 months



 

After waves of AI winters, finally Artificial Intelligence is witnessing it’s longest and warmest summer with the real world applications taking hold and solving things. This boom in in AI has also led to active development of various frameworks and libraries which help you implement the technology. 

 

So let’s take a look at some of these latest Opensource AI and machine learning libraries and frameworks.

 

1. TensorFlow

 

 

First in the list is the most popular and trending machine learning library called TensorFlow. The first version of the library was released in the year 2015 and since then it has snowballed into a massive vibrant community (reaching 100,000 stars on github). It started its life from Google to make sense of massive amounts of data it was producing and currently is used by most tech companies. It allows users to build neural networks or other ML algorithms. 

 

https://www.tensorflow.org/

 

2. Scikit-Learn

 

 

It is built on NumPy, SciPy, and matplotlib and focusses on data mining and analysis. It was reeased in the year 2007 and since then has developed good following among data scientists and researchers. Scikit is written in python language and can be used to implement multiple different types of machine learning models like classification, regression, clustering etc. 

 

http://scikit-learn.org/

 

3. Keras

 

 

Keras is a library which is written as set of high level neural network APIs and is capable of running on top of other ml libraries like TensorFlow, Theano and Microsoft Cognitive Toolkit. Simple purpose of Keras is to help people go from idea stage to actual prototype in fastest possible way by providing a simple, user friendly and modular approach. 

 

https://keras.io/

 

4. Caffe 

 

 

Developed at Berkeley AI Research and released in the year 2007, Caffe provides an expressive extensive way to write machine learning models. Caffe stands for Convolutional Architecture for Fast Feature Embedding and is written in c++ and comes with in an inbuilt python interface. 

 

http://caffe.berkeleyvision.org/

 

5. Theano

 

 

Theano is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Theano features:

  • tight integration with NumPy — Use numpy.nd array in Theano-compiled functions.
  • transparent use of a GPU — Perform data-intensive computations much faster than on a CPU.
  • efficient symbolic differentiation — Theano does your derivatives for functions with one or many inputs.
  • speed and stability optimizations — Get the right answer for log(1+x) even when x is really tiny.
  • dynamic C code generation — Evaluate expressions faster.
  • extensive unit-testing and self-verification — Detect and diagnose many types of errors.

Theano has been powering large-scale computationally intensive scientific investigations since 2007.

 

https://github.com/Theano/Theano

 

6. Torch

 

 

Torch is a ml library written in Lua and has an underlying C implementation. its main features are n -dimensional array, linear algebra routines, numeric routines and efficient GPU support. 

Pytorch another very popular library is written on top of Torch. 

 

You can see that there are various different options to implement machine learning or AI -first applications, so select wisely based on your requirement and resources. 

 

http://torch.ch/

 

Let us know know which one of these frameworks have you used before or would like to use in the future. Also let us know if any other popular machine learning framework needs to be added to the list.

 



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