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

#machine-learning #tensorflow #deep-learning #python