Course Content
Introduction
TensorFlow is an open-source and free library for differentiable programming and data flow across a variety of tasks. TensorFlow is a math library and it is used for machine learning applications for example neural networks.
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What is Python TensorFlow?
Key Terms
Tensorflow Architecture Component
Life Cycle of TensorFlow Servable
Installing Python Tensorflow
Algorithms in TensorFlow
Creating a Tensor
Tensor Attributes
TensorFlow Useful Functions
TensorFlow Variables
TensorFlow Placeholder
TensorFlow Session
Simple Tensorflow Program
Methods to Load Data
TensorFlow Tutorial
    About Lesson

    TensorFlow is an open-source and free library for differentiable programming and data flow across a variety of tasks. TensorFlow is a math library and it is used for machine learning applications for example neural networks.

    TensorFlow’s flexible architecture enables the ease of deployment of computation across a variety of platforms (CPUs, TPUs, and GPUs), and from desktops to clusters of servers to mobile and edge devices.

    TensorFlow computations are expressed as stateful dataflow graphs. The name of TensorFlow is determined from the operations that neural networks perform on multidimensional data arrays, which are referred to as tensors.

    In 2018, Google released TensorFlow.js version 1.0 for TensorFlow Graphics for deep learning in computer graphics and machine learning in JavaScript.

    TensorFlow was developed by the Google Brain team for its internal use. It was released under the Apache License 2.0. The official website is www.tensorflow.org.

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