5 of the Best Free Software Stacks for AI Development

If you're an engineer, designer, or maker looking to get into developing artificial intelligence and machine learning applications, look no further than these five options.
  • Advances in artificial intelligence (AI) and machine learning (ML) have had a significant impact on the industrial, consumer, automotive, and entertainment markets. Coinciding with this there have been significant developments in the open source movement, where software stacks and libraries have allowed makers, engineers, and designers the creativity to build truly smart products for the home, school, industry, and business settings.

    But with an ever-growing number of options available, where does a developer start when it comes to AI? Here are five of the most popular (and free!) software stacks for AI and ML applications development.

    Click through the slideshow to see the options. 

  • Keras

    Keras is an open-source platform dedicated to neural network applications. The Keras neural network library was written in the Python programming language. The open-source platform can operate on top of the TensorFlow, Microsoft Cognitive Toolkit, Theano, or PlaidML software stacks. One of Keras’ appealing features is being modular, user friendly, and extensible. Keras is designed for front-end applications for web and server products. Therefore, a disadvantage of Keras is the loss of functionality when working on backend resources required for front- end framework applications.

  • Pandas

    Pandas was developed for the Python programming language for data analytics applications. The two words panel data is the descriptive origin of Pandas. Pandas is a free software package released under the Berkeley Software Distribution (BSD) three clause license. The two targeted data analytics applications are data manipulation operations and analysis. In addition, Pandas includes: numerical tables manipulation; time series; and data structure. The observations based on multiple time periods is the classification of Pandas. The disadvantage of Pandas is not being able to integrate well with Structure Query Language (SQL). The integration loss is in query optimization or access restriction for obtaining SQL data.

  • PyTorch

    PyTorch is a machine learning library software package for the Python programming language released under the BSD license. PyTorch’s software architecture is based on the Torch library. Torch is used for natural language processing associated with ML applications. Two high level features of PyTorch include Tensor computing and deep neural networks. A disadvantage of PyTorch is that there is no official version 1.0 release. Therefore, PyTorch is not yet stable for authentic production work.

  • Scikit-learn

    Scikit-learn, formerly known as scikit. learn, is a free ML library that can be used with the popular Python programming language. Some of scikit. learn software features include: classification; regression; and clustering algorithms. Support Vector machines (SVM), random forests, gradient boosting, k-means, and DBScan are software algorithms used in various ML applications. These software support algorithms were designed to work effectively with Python’s numerical and scientific libraries. The popular numerical and scientific Python libraries used in ML applications are NumPy and SciPy.

    Scikit-learn has a major disadvantage, however. The lack of statistical operations focus prevents such statistic functions as p-value, R^2, p-statistics and variable inflation tracking allows limiting data mining capabilities with the ML library.

  • TensorFlow

    TensorFlow is a free, open-source library for managing data workflow. TensorFlow is based off a tensor which is a n-multidimensional array used in high level mathematics processing of large data sets. In addition, the mathematics data processing is managed through tensors symbols math library. The two main applications for TensorFlow is machine learning and neural networks. The disadvantage of TensorFlow is the lack of speed and usage when processing very larger n-dimensional array data sets required for ML application training sessions.

  • Here's a comparison chart of all the software stacks.
    Google’s Colaboratory allows for free testing and experimentation of these popular AI/ML development tools. Additional information and resources on how to explore AI/ML concepts using Google Colaboratory on a Raspberry Pi can be found here.

Don Wilcher is a passionate teacher of electronics technology and an electrical engineer with 26 years of industrial experience. He’s worked on industrial robotics systems, automotive electronic modules/systems, and embedded wireless controls for small consumer appliances. He’s also a book author, writing DIY project books on electronics and robotics technologies.

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