NumPy using TensorFlow : A Faster Way To Do NumPy Operations

NumPy using TensorFlow : A Faster Way To Do NumPy Operations

All The Benefits of The TensorFlow Ecosystem To NumPy! NumPy is one of the most used library of Python in Data Science field. TensorFlow NumPy implements a subset of the full NumPy spec. While more symbols will be added over time, there are systematic features that will not be supported in the near future. NumPy using TensorFlow : A Faster Way To Do NumPy Operations

NumPy is one of the most used library of Python in Data Science field. TensorFlow NumPy implements a subset of the full NumPy spec. While more symbols will be added over time, there are systematic features that will not be supported in the near future.

NOTE: If you don’t know about NumPy library you should visit this article “ NumPy: Everything A Data Scientist Should Know”.

NumPy API on TensorFlow :

All the benefits of TensorFlow Ecosystem for NumPy:

  • Accelerate Numpy code on CPU/TPU/GPU.
  • Auto-Differentiate through NumPy code.
  • Optimize execution using compilation and auto-vectorization.
  • Run distributed using tf.distribute .
  • Combine seamlessly with TensorFlow APIs (tf.linalg , tf.signal , tf.data , tf.keras ).
  • SavedModel Serialization.

Contents

  • Getting started
  • Performance Comparison
  • TensorFlow NumPy support and flexibility
  • Interoperability with NumPy and TensorFlow
  • Adding new NumPy operations
  • Case studies

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