MARC details
| 000 -LEADER |
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| fixed length control field |
05127nam a22004815i 4500 |
| 001 - CONTROL NUMBER |
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| control field |
ssj0002088267 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
WaSeSS |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20220422162523.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS |
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| fixed length control field |
m d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
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| fixed length control field |
cr n |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
180912s2018 xxu| o |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781484238738 |
| 024 7# - OTHER STANDARD IDENTIFIER |
|---|
| Standard number or code |
10.1007/978-1-4842-3873-8 |
| Source of number or code |
doi |
| 040 ## - CATALOGING SOURCE |
|---|
| Modifying agency |
WaSeSS |
| Transcribing agency |
AIMIT LIBRARY |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
Q334-342 |
| 072 #7 - SUBJECT CATEGORY CODE |
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| Subject category code |
UYQ |
| Source |
bicssc |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
COM004000 |
| Source |
bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE |
|---|
| Subject category code |
UYQ |
| Source |
thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
006.31 |
| Item number |
AMUM |
| 092 ## - LOCALLY ASSIGNED DEWEY CALL NUMBER (OCLC) |
|---|
| Classification number |
EBOOK |
| 999 ## - SYSTEM CONTROL NUMBERS (KOHA) |
|---|
| Koha Dewey Subclass [OBSOLETE] |
03656728 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Amunategui, Manuel. |
| 9 (RLIN) |
30928 |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Monetizing Machine Learning : |
| Remainder of title |
quickly turn python ml Ideas into web applications on the serverless cloud / |
| Statement of responsibility, etc. |
By Manuel Amunategui and Mehdi Roopaei. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Berkeley, CA : |
| Name of publisher, distributor, etc. |
Apress : |
| -- |
Imprint: Apress , |
| Date of publication, distribution, etc. |
2018. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxi,482p. ; |
| Other physical details |
PB |
| Dimensions |
24.3 cm. |
| 347 ## - DIGITAL FILE CHARACTERISTICS |
|---|
| File type |
text file |
| Encoding format |
PDF |
| Source |
rda |
| 505 0# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
Chapter 1 Introduction to Serverless Technologies -- Chapter 2 Client-Side Intelligence using Regression Coefficients on Azure -- Chapter 3 Real-Time Intelligence with Logistic Regression on GCP -- Chapter 4 Pre-Trained Intelligence with Gradient Boosting Machine on AWS -- Chapter 5 Case Study Part 1: Supporting Both Web and Mobile Browsers -- Chapter 6 Displaying Predictions with Google Maps on Azure -- Chapter 7 Forecasting with Naive Bayes and OpenWeather on AWS -- Chapter 8 Interactive Drawing Canvas and Digit Predictions using TensorFlow on GCP -- Chapter 9 Case Study Part 2: Displaying Dynamic Charts -- Chapter 10 Recommending with Singular Value Decomposition on GCP -- Chapter 11 Simplifying Complex Concepts with NLP and Visualization on Azure -- Chapter 12 Case Study Part 3: Enriching Content with Fundamental Financial Information -- Chapter 13 Google Analytics -- Chapter 14 A/B Testing on PythonAnywhere and MySQL -- Chapter 15 From Visitor To Subscriber -- Chapter 16 Case Study Part 4: Building a Subscription Paywall with Memberful -- Chapter 17 Conclusion.-. |
| 506 ## - RESTRICTIONS ON ACCESS NOTE |
|---|
| Terms governing access |
Requires an SPL library card. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
Take your Python machine learning ideas and create serverless web applications accessible by anyone with an Internet connection. Some of the most popular serverless cloud providers are covered in this book-Amazon, Microsoft, Google, and PythonAnywhere. You will work through a series of common Python data science problems in an increasing order of complexity. The practical projects presented in this book are simple, clear, and can be used as templates to jump-start many other types of projects. You will learn to create a web application around numerical or categorical predictions, understand the analysis of text, create powerful and interactive presentations, serve restricted access to data, and leverage web plugins to accept credit card payments and donations. You will get your projects into the hands of the world in no time. Each chapter follows three steps: modeling the right way, designing and developing a local web application, and deploying onto a popular and reliable serverless cloud provider. You can easily jump to or skip particular topics in the book. You also will have access to Jupyter notebooks and code repositories for complete versions of the code covered in the book. What You'll Learn: Extend your machine learning models using simple techniques to create compelling and interactive web dashboards Leverage the Flask web framework for rapid prototyping of your Python models and ideas Create dynamic content powered by regression coefficients, logistic regressions, gradient boosting machines, Bayesian classifications, and more Harness the power of TensorFlow by exporting saved models into web applications Create rich web dashboards to handle complex real-time user input with JavaScript and Ajax to yield interactive and tailored content Create dashboards with paywalls to offer subscription-based access Access API data such as Google Maps, OpenWeather, etc. Apply different approaches to make sense of text data and return customized intelligence Build an intuitive and useful recommendation site to add value to users and entice them to keep coming back Utilize the freemium offerings of Google Analytics and analyze the results Take your ideas all the way to your customer's plate using the top serverless cloud providers. |
| 538 ## - SYSTEM DETAILS NOTE |
|---|
| System details note |
Mode of access: World Wide Web. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence |
| 9 (RLIN) |
30929 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Computer Communication Networks. |
| 9 (RLIN) |
30930 |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data |
| 9 (RLIN) |
30931 |
| 655 #7 - INDEX TERM--GENRE/FORM |
|---|
| Genre/form data or focus term |
Electronic books. |
| Source of term |
local |
| 9 (RLIN) |
30932 |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Roopaei, Mehdi. |
| 9 (RLIN) |
30933 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484238721 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484238745 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
|---|
| Relationship information |
Printed edition: |
| International Standard Book Number |
9781484245576 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Link text |
View this electronic item in O'Reilly Online Learning: Academic/Public Library Edition. |
| Uniform Resource Identifier |
<a href="https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484238738/?ar">https://ezproxy.spl.org/login?url=https://learning.oreilly.com/library/view/~/9781484238738/?ar</a> |
| Public note |
An e-book available through full-text database. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Book |
| Edition |
1st |
| Call number prefix |
006.31 AMUM |
