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Neural Network Programming with TensorFlow

You're reading from Neural Network Programming with TensorFlow Unleash the power of TensorFlow to train efficient neural networks

Product type Paperback

Published in Nov 2017

Publisher Packt

ISBN-13 9781788390392

Length 274 pages

Edition 1st Edition

Languages

Python

Tools

TensorFlow

Concepts

Neural Networks

Authors (2):

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

Table of Contents (11) Chapters

Preface

1. Maths for Neural Networks FREE CHAPTER

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

What is optimization?

The process to find maxima or minima is based on constraints. The choice of optimization algorithm for your deep learning model can mean the difference between good results in minutes, hours, and days.

Note

Optimization sits at the center of deep learning. Most learning problems reduce to optimization problems. Let's imagine we are solving a problem for some set of data. Using this pre-processed data, we train a model by solving an optimization problem, which optimizes the weights of the model with regards to the chosen loss function and some regularization function.

Hyper parameters of a model play a significant role in the efficient training of a model. Therefore, it is essential to use the different optimization strategies and algorithms to measure appropriate and optimum values of model's hyper parameters, which affect our Model's learning process, and finally the output of a model.

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Authors (2)

Dua

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.

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Ghotra

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 Ghotra

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