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Applications of Machine Learning for research and industry purposes with Tensorflow

By Vinay Varma.

Group: DevoWormML

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A Brief Introduction to Tensorflow Tools

  1. Tensorflow core. (tensorflow.org)
  2. Provides Low level Module (tf.nn) and High Level Module (tf.keras)
  3. Provides Three API’s (Sequential, Functional and Subclassing).

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Sequential

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Functional

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Subclassing

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2. Tensorflow Extended( tensorflow.org/tfx/ ) for production and analysis.

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Tensorflow Serving

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3. Tensorflow.js (tensorflow.org/js/)

  • Can write Neural Networks in JS or convert python models to run in browser.
  • Can load pre-trained models and re-train them in browser.
  • Deploy our models using pure JS.

Advantages:

  1. No setup required. No need to install anything.
  2. Privacy.
  3. No lag in real time video processing.

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4. Tensorflow Lite (tensorflow.org/lite)

  • Can load a trained model on an embedded system and run inference.
  • Can trian lite weight models on the chip itself with python,

Advantages:

  1. Makes edge computing possible.
  2. A major advantage in the electronics industry.

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5. Tensorflow Hub (www.tensorflow.org/hub/)

  • Load pre-trained models with just one line of code.
  • Easy to share.

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Modules are composable, reusable, and re-trainable.

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6. Tensorflow Datasets (tensorflow.org/datasets)

  • Fast, Flexible and easy to use.

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6. Tensorflow Datasets

  • Fast, Flexible and easy to use.

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