TensorFlow for Machine learning and Deep learning

Tensorflow is an open source software library for numerical computation by using data flow graphs. In graphs, nodes represent mathematical operations, edges represent multidimensional data known as tensors.Computations can be deployed on one or more CPU or GPU in desktop or mobile.

TensorFlow for Machine learning and Deep learning

  • Open source software library created by Google.
  • A library for dataflow programming.
  • As we know that both Machine learning and Deep learning have a pool of powerful algorithms-and both works to skilled a computer to learn automatically complex problem and make a decision and provide solution.
  •  It leverages various optimization techniques to make the calculation of mathematical expressions easier and more performing. Because of this, it is becoming heart of Machine learning and Deep Learning.

Some of the key features of TensorFlow are:

  • Tensorflow is implemented in C++ and is available for C language and Python
  • Efficiently works with mathematical expressions involving multi-dimensional arrays
  • Good support of deep neural networks and machine learning concepts
  • GPU/CPU computing where the same code can be executed on both architectures
  • High scalability of computation across machines and huge data sets

Together, these features make TensorFlow the perfect framework for machine intelligence at a production scale.
If you’re interested in details please refer the below links

  1. In this link , you will learn how you can use simple yet powerful machine learning methods in TensorFlow and how you can use some of its auxiliary libraries to debug, visualize, and tweak the models created with it.
  2. In this link you will learn some new features of TensorFlow’s 1.4 release
    We will keep updated you as will go through the good articles.

For complete details on Tensorflow refer https://www.tensorflow.org/tutorials/