Google Launches TensorFlow ML Framework

Google, on 3rd September 2019, introduced TensorFlow Machine Learning Framework named Neural Structured Learning (NSL). It is an open framework which makes use of Neural Graph to train Neural Networks with graphs and structured information.



TensorFlow is an open-source machine learning library widely used in various areas of research and development. TensorFlow offers APIs for beginner as well as expert level to develop applications for platforms not only for desktop, but also for web, cloud and smaller devices like mobile, smart watch etc. TensorFlow is considered to be a comprehensive ecosystem of tools, libraries and resources by researchers which lets them explore more in their area of interest using Machine Learning. On the other hand, TensorFlow allows developers to easily build and deploy Machine Learning applications.

Neural Structured Learning

The NSL framework consists of tools to help developers structure the input data and APIs to create examples for adversarial training. NSL is not restricted only to supervised training, but it also allows semi-supervised and unsupervised learning approach to create models. The most fascinating fact about NSL is that it can be written in less than five lines of code on structured signals for regularization during training.

NSL can be used to train Neural Networks by supplementing graphical data with the input features. Structured signals basically represent relationship between labeled and unlabeled data. So, the use of NSL during Neural Networks training makes it possible to make use of both labeled and unlabeled data. Therefore, researchers and developers can continue to work without worrying about less amount of available data.
NSL works with TensorFlow platform and can be used by both, experienced and unskilled machine learning practitioners. NSL has capability to be used for developing models of Natural Language Processing and Computer Vision. It can also perform predictions of future events in almost all the domains including those from graphical datasets like medical records etc.

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Advantages of NSL


The use of structured signals during training has two-fold advantages. First, it helps developers to achieve higher model accuracy, especially when relatively small labeled dataset is available. Second, the training results into a robust model. Hence, according to Google, this can be used in improving model performance.
NSL can generalize to both; Neural Graph Learning and Adversarial Learning. It offers developers following easy to use tools and APIs letting them train their models:

  • Keras APIs: These allow training models with graphs i.e. explicit structure and adversarial perturbations i.e. implicit structure.
  • TensorFlow Operations and Functions: These enable training models using low-level TensorFlow APIs.
  • Tools: These are used to construct graphs during training.

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