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Overview Machine Learning and Deep Learning Frameworks

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The Digital Journey of Banking and Insurance, Volume III

Abstract

This chapter provides an overview of the different machine learning (ML) and deep learning (DL) frameworks, aiming to show the variety ranging from different open-source initiatives through to standard software vendors and specialized start-ups contributing to the enormous amount of tools to analyze, condense and predict data.

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Notes

  1. 1.

    See Kopic et al. (2019).

  2. 2.

    See Akhgarnush et al. (2019).

  3. 3.

    See Steurer (2021).

  4. 4.

    While it has become more and more common to buy successful companies (start-up or established companies) to lower the development risk (or, to be more precise, to transform the development risk into an integration risk).

  5. 5.

    The initiator of Kafka, see Steurer (2021).

  6. 6.

    Google did not initiate Hadoop but contributed to the concept (BigFiles and later Google File System), which had an impact on Hadoop.

  7. 7.

    See The Apache Software Foundation (2020).

  8. 8.

    See Bettio et al. (2019) and The Linux Foundation, Hyperledger (2020).

  9. 9.

    See The Linux Foundation, LF AI & Data (2020).

  10. 10.

    Customer relationship management.

  11. 11.

    Next best offer, Next best action (see May 2019).

  12. 12.

    Microsoft is located somewhere between the standard software vendors and Big Tech (GAFA or GAFAM) and has a special role in the ML/DL framework area.

  13. 13.

    The Microsoft Cognitive Toolkit.

  14. 14.

    Referring to the continent, or New Zealand, to be precise.

  15. 15.

    E.g. KNIME (Konstanz Information Miner).

  16. 16.

    Caffe was replaced by Caffe2 in 2017 and then integrated into PyTorch by Facebook.

  17. 17.

    Especially the American Big Techs (GAFA), and we have already discussed the localization of Microsoft (standard software vendor or Big Tech) in this chapter.

  18. 18.

    Due to their “deep pockets” filled with revenues from advertising (particularly Google and Facebook) or other differentiating activities like Amazon’s cloud services.

  19. 19.

    Like GNU General Public License (GPL), GNU Lesser General Public License (LGPL), Mozilla Public License, BSD license (Berkeley Software Distribution) or Apache Software License (for details see Sect. 2.3 in Liermann, Open-Source Software 2021).

  20. 20.

    Application-specific integrated circuit.

  21. 21.

    Graphics processing unit.

  22. 22.

    Convolutional neural networks or Deep convolutional networks (DCN), see Sect. 3.6 in Liermann et al., Deep learning—an introduction (2019).

  23. 23.

    Recurrent neural network, see Sect. 3.2 in Liermann et al., Deep learning—an introduction (2019).

  24. 24.

    Baidu is a multinational technology company focused on services and products in an internet-related context and artificial intelligence (AI). Baidu is one of the Chinese Big Tech companies (BATX).

  25. 25.

    Compute Unified Device Architecture by Nvidia is an application programming interface (API) model and a parallel computing platform.

  26. 26.

    Caffe/Caffe2, Chainer, Microsoft Cognitive Toolkit, MATLAB, Mxnet, PaddlePaddle, PyTorch, Tensorflow, Torch and Mathematica’s Wolfram Language.

  27. 27.

    Intel, Nvidia, Microsoft and IBM.

  28. 28.

    Also referred to as artificial general intelligence (AGI) or full AI.

  29. 29.

    See Baum (2017).

  30. 30.

    Also referred to as narrowed AI.

  31. 31.

    It is to be expected that things will have already moved forward by the time the book is published.

  32. 32.

    See Dlib (2020).

  33. 33.

    See H2O.ai, h2o.ai Overview (2020).

  34. 34.

    Formerly known as Data Science Experience or DSX.

  35. 35.

    See also Microsoft Corporation (2020).

  36. 36.

    GBT—Gradient boosted trees.

  37. 37.

    GBDT—Gradient boosted decision trees.

  38. 38.

    GBRT—Gradient boosted regression trees.

  39. 39.

    GBM—Gradient boosting machine.

  40. 40.

    MART—Multiple additive regression trees.

  41. 41.

    Intelligent optimization is automated creation and selection of improving solutions.

  42. 42.

    Python module that provides Python bindings for ML.NET.

  43. 43.

    Armadillo is a library for linear algebra (see Conrad Sanderson and Ryan Curtin 2020).

  44. 44.

    RCASE offers methods aiming to identify the root causes of faults or problems. It has applications—among others—in incomplete data and dirty data cleansing.

  45. 45.

    Density-Based Spatial Clustering of Applications with Noise is a popular data mining algorithm for cluster analysis (see Ester et al. 1996).

  46. 46.

    See XGBoost Contributors (2020).

  47. 47.

    GBM—Gradient boosting machine.

  48. 48.

    GBRT—Gradient boosted regression trees.

  49. 49.

    GBDT—Gradient boosted decision trees.

  50. 50.

    Sometimes referred as The Microsoft Cognitive Toolkit.

  51. 51.

    YOLO is a one-step, real-time object detection system. A one-step detection strategy means that the images to be analyzed need to be read only once.

  52. 52.

    See fast.ai (2020).

  53. 53.

    See flux (2020).

  54. 54.

    Julia is a high-level programming language targeting numerical and scientific computing. Julia can also be used as a general-purpose language with high execution speed. Julia was created with the goal of being as easy on statistics as R is.

  55. 55.

    DeepMind (formerly Google DeepMind) is a company specializing in artificial intelligence (AI) programming. DeepMind was founded in September 2010 and acquired by Google Inc. in 2014.

  56. 56.

    The following libraries are supported: Caffe, TensorFlow, Torch, Darknet, models in ONNX format.

  57. 57.

    See plaidML (2020).

  58. 58.

    Application-specific chips, to support and accelerate machine learning optimized for tensor operations.

  59. 59.

    NER (Named Entity Recognition), Word embeddings and Seq2Seq models (see Sect. 4 in Liermann et al., Deep learning—an introduction 2019).

  60. 60.

    Including to some extent Natural Language Understanding and Natural Language Generation.

  61. 61.

    For inside and outside digitalization (see Liermann and Stegmann, Introduction 2021).

  62. 62.

    clinical Text Analysis and Knowledge Extraction System.

  63. 63.

    Unstructured Information Management Architecture framework. UIMA was initially developed by IBM.

  64. 64.

    Bidirectional Encoder Representations from Transformers.

  65. 65.

    Systematic Role Labeling.

  66. 66.

    SYSTRAN (SYStem TRANslation) is a software system for machine translation founded in the 1960s.

  67. 67.

    Ubisqus is a language service provider.

  68. 68.

    For the NPL frameworks BERT, DistilBert, GPT-2, RoBERTa, XLM and XLNet.

  69. 69.

    Natural Language Understanding.

  70. 70.

    Natural Language Generation.

  71. 71.

    Extraction, transformation, loading (ETL).

  72. 72.

    Analysis, visualization and reporting.

  73. 73.

    See QuantumBlack (2020).

  74. 74.

    Pip—a Python package-management system—targets installing and managing Python software packages.

  75. 75.

    See ROOT/TMVA team (2020).

  76. 76.

    Driven by the algorithmic demand (for a simple example see subsection 2.1.4 in Liermann et al., Deep learning—an introduction 2019) for training the deep learning networks, which is to some extent similar to the requirements for 3D graphics.

  77. 77.

    The programming language is a superset of the Python programming language and aims to give C-like performance to most code parts in Python with additional C-oriented syntax.

  78. 78.

    R is a free software environment and programming language for statistical computing and graphic representation. The development started in 1993.

  79. 79.

    S is a statistical programming language developed in 1970 at Bell Laboratories.

  80. 80.

    The R package reticulate offers an integration of python code to R see Ushey (2020).

  81. 81.

    The SAS language is a statistical analysis programming language designed by Anthony James Barr at North Carolina State University and is the standard language for the SAS software modules.

  82. 82.

    App programming language of iOS and other Apple operating systems.

  83. 83.

    Kotlin designed for interoperability with Java. Kotlin’s JVM version is based on the Java Class Library.

  84. 84.

    See The Linux Foundation, Open Neural Network Exchange (2020).

  85. 85.

    Allow developers to more easily move between frameworks, some of which may be more desirable for specific phases of the development process, such as fast training, network architecture flexibility or inferencing on mobile devices.

  86. 86.

    Allow hardware vendors and others to improve the performance of artificial neural networks of multiple frameworks at once by targeting the ONNX representation.

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    Volker Liermann

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    Volker Liermann

  2. ifb Americas, Inc., Charlotte, NC, USA

    Claus Stegmann

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Liermann, V. (2021). Overview Machine Learning and Deep Learning Frameworks. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume III. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78821-6_12

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