Skip to Main Content
Deep Learning and its Applications using Python
book

Deep Learning and its Applications using Python

by Niha Kamal Basha, Surbhi Bhatia Khan, Abhishek Kumar, Arwa Mashat
October 2023
Beginner to intermediate content levelBeginner to intermediate
256 pages
5h 42m
English
Wiley-Scrivener
Content preview from Deep Learning and its Applications using Python

2Basics of TensorFlow

This chapter explains TensorFlow fundamentals based on deep learning framework. It plays a major role in pattern recognition, specifically about language, images, sound, and time-series data. Classification, prediction, clustering, and feature extraction have been done with the help of deep learning. Favorably, TensorFlow released in November 2015 by Google and its evolution are tabulated in Table 2.1.

The aim of this chapter is to explain the basic components of TensorFlow. TensorFlow [10] has a facility for performing partial sub-graph computation to agree distributed training by partitioning the neural networks. In addition to that, TensorFlow agrees model parallelism as well as data parallelism. TensorFlow also offers numerous APIs. The deepest level API has named as TensorFlow Core, which provide wide-ranging programming control. The important features of TensorFlow have been enumerated below:

  • Its graph deals an illustration of computations.
  • Its graph has nodes used for operations.
  • It performs computation within stipulated period.
  • A graph for computation must be launched in a session.
  • The devices such as CPU and GPU places the graph operations in a session.
  • For executing graph operations, a session, which has methods have been used.

2.1 Tensors

Initially, the basic of TensorFlow have been discussed. It is a mathematical object. A multidimensional array is used for representation. A tensor [11] of rank one is vector/array whereas tensor of rank ...

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.
Start your free trial

You might also like

Python Deep Learning - Third Edition

Python Deep Learning - Third Edition

Ivan Vasilev, Valentino Zocca
Generative Deep Learning with Python

Generative Deep Learning with Python

Cuantum Technologies LLC
Data-Centric Machine Learning with Python

Data-Centric Machine Learning with Python

Jonas Christensen, Nakul Bajaj, Manmohan Gosada
Math for Deep Learning

Math for Deep Learning

Ronald T. Kneusel

Publisher Resources

ISBN: 9781394166466Purchase Link