Chapter: Optimization for Machine Learning: Gradient Descent

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Home > Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners > Optimization for Machine Learning: Gradient Descent

Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners |

Ekaba Bisong

Apress

Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners

Optimization for Machine Learning: Gradient Descent

Chapter Info

Authors

Ekaba Bisong

ARI Id

1665215555449_5400836

Access

Not Available Free

Pages

203-207

DOI

10.1007/978-1-4842-4470-8_16

Chapter URL

https://rd.springer.com/chapter/10.1007/978-1-4842-4470-8_16

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Chapters/Headings	Author(s)	Pages	Info
Front Matter		i-xxix
Front Matter		1-1
What Is Cloud Computing?	Ekaba Bisong	3-6
An Overview of Google Cloud Platform Services	Ekaba Bisong	7-10
The Google Cloud SDK and Web CLI	Ekaba Bisong	11-24
Google Cloud Storage (GCS)	Ekaba Bisong	25-33
Google Compute Engine (GCE)	Ekaba Bisong	35-48
JupyterLab Notebooks	Ekaba Bisong	49-57
Google Colaboratory	Ekaba Bisong	59-64
Front Matter		65-65
What Is Data Science?	Ekaba Bisong	67-70
Python	Ekaba Bisong	71-89
NumPy	Ekaba Bisong	91-113
Pandas	Ekaba Bisong	115-150
Matplotlib and Seaborn	Ekaba Bisong	151-165
Front Matter		167-167
What Is Machine Learning?	Ekaba Bisong	169-170
Principles of Learning	Ekaba Bisong	171-197
Batch vs. Online Learning	Ekaba Bisong	199-201
Optimization for Machine Learning: Gradient Descent	Ekaba Bisong	203-207
Chapters/Headings	Author(s)	Pages	Info

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