Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Basic TensorFlow

  • Chapter
  • First Online:
Beginning Deep Learning with TensorFlow

Abstract

TensorFlow is a scientific computing library of deep learning algorithms. All operations are performed based on tensor objects. Complex neural network algorithms are essentially a combination of basic operations such as multiplication and addition of tensors. Therefore, it is important to get familiar with the basic tensor operation in TensorFlow. Only by mastering these operations can we realize various complex and novel network models at will and understand the essence of various models and algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 49.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 64.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zeng, X., Long, L. (2022). Basic TensorFlow. In: Beginning Deep Learning with TensorFlow. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-7915-1_4

Download citation

Publish with us

Policies and ethics