In the world of machine learning, there are thousands of different techniques and algorithms that can be applied to a problem in order to help you arrive at the right solution. With so many different approaches available, choosing which one to apply can be a time-consuming and difficult process. To simplify this process, we have put together a list of the five best Python machine learning frameworks and libraries that will make it much easier to create your own machine learning apps by automating many of the data preparation and model building processes behind the scenes.
1) TensorFlow
TensorFlow is an open-source software library for dataflow programming across a range of tasks. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. TensorFlow was originally developed by researchers and engineers working on the Google Brain Team within Google’s Machine Intelligence research organization for internal applications. The system is general enough to be applicable in a wide variety of other domains, as well.
2) Scikit-Learn
Scikit-learn is a powerful machine learning library for python. Scikit-learn makes it easy to explore different algorithms and machine learning concepts. This open-source library comes with pre-processors, classifiers, clustering tools, dimensionality reduction techniques, and more. Scikit-learn supports Windows, Mac OS X, Linux/Unix operating systems. There are 2 subprojects under scikit learn viz ml_pipeline & scikit_bioinfomatic.
3) Keras
Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
4) MXNet
MXNet is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize performance and scalability in production systems. MXNet is particularly useful when working with real-time data streams or distributed computing environments. Hire python developer to use MXNet.
5) PyTorch
Deep learning framework that allows for rapid prototyping and research. PyTorch was released as an open-source project in October 2016 by Facebook AI Research (FAIR). PyTorch is used for building dynamic neural networks. It supports GPU acceleration via CUDA or OpenCL, with automatic differentiation of complex neural network models.
Conclusion
Choosing a machine learning framework requires a specific set of considerations. Do you need to process streaming data? Are you building neural networks or other advanced techniques? Each framework above has its strengths and weaknesses, and we encourage you to consider your options before making your decision. That said, if we had to pick one (and maybe we do), Keras is our top choice for enterprise-ready applications which is used majorly by top machine learning companies.
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