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Neural Network Programming with TensorFlow

You're reading from Neural Network Programming with TensorFlow

Product type Book

Published in Nov 2017

Publisher Packt

ISBN-13 9781788390392

Pages 274 pages

Edition 1st Edition

Languages

Concepts

Neural Networks

Authors (2):

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

Table of Contents (17) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Maths for Neural Networks

2. Deep Feedforward Networks

3. Optimization for Neural Networks

4. Convolutional Neural Networks

5. Recurrent Neural Networks

6. Generative Models

7. Deep Belief Networking

8. Autoencoders

9. Research in Neural Networks

10. Getting started with TensorFlow

Chapter 1. Maths for Neural Networks

Neural network users need to have a fair understanding of neural network concepts, algorithms, and the underlying mathematics. Good mathematical intuition and understanding of many techniques is necessary for a solid grasp of the inner functioning of the algorithms and for getting good results. The amount of maths required and the level of maths needed to understand these techniques is multidimensional and also depends on interest. In this chapter, you will learn neural networks by understanding the maths used to solve complex computational problems. This chapter covers the basics of linear algebra, calculus, and optimization for neural networks.

The main purpose of this chapter is to set up the fundamentals of mathematics for the upcoming chapters.

Following topics will be covered in the chapter:

Understanding linear algebra
Understanding Calculus
Optimization

You have been reading a chapter from

Neural Network Programming with TensorFlow

Published in: Nov 2017 Publisher: Packt ISBN-13: 9781788390392

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Authors (2)

Manpreet Singh Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

See other products by Manpreet Singh Ghotra

Rajdeep Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

See other products by Rajdeep Dua

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