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Mastering Azure Machine Learning

You're reading from   Mastering Azure Machine Learning Perform large-scale end-to-end advanced machine learning in the cloud with Microsoft Azure Machine Learning

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Product type Paperback
Published in Apr 2020
Publisher Packt
ISBN-13 9781789807554
Length 436 pages
Edition 1st Edition
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Authors (2):
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Christoph Körner Christoph Körner
Author Profile Icon Christoph Körner
Christoph Körner
Kaijisse Waaijer Kaijisse Waaijer
Author Profile Icon Kaijisse Waaijer
Kaijisse Waaijer
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Toc

Table of Contents (20) Chapters Close

Preface Section 1: Azure Machine Learning
1. Building an end-to-end machine learning pipeline in Azure FREE CHAPTER 2. Choosing a machine learning service in Azure Section 2: Experimentation and Data Preparation
3. Data experimentation and visualization using Azure 4. ETL, data preparation, and feature extraction 5. Azure Machine Learning pipelines 6. Advanced feature extraction with NLP Section 3: Training Machine Learning Models
7. Building ML models using Azure Machine Learning 8. Training deep neural networks on Azure 9. Hyperparameter tuning and Automated Machine Learning 10. Distributed machine learning on Azure 11. Building a recommendation engine in Azure Section 4: Optimization and Deployment of Machine Learning Models
12. Deploying and operating machine learning models 13. MLOps—DevOps for machine learning 14. What's next? Index

Index

About

All major keywords used in this book are captured alphabetically in this section. Each one is accompanied by the page number of where they appear.

accelerate: 18, 368-369

active: 18, 117, 158, 333, 404

acyclic: 129, 151, 350

adaboost: 222

adadelta: 316

adapters: 140

aggregate: 86, 372

algorithm: 4, 16, 22, 27-28, 30-31, 38-39, 53-54, 101, 105, 107, 176-177, 190-191, 195-199, 203, 220-225, 253, 258, 286, 309-310, 326, 337-338, 342-343, 352, 403

amazon: 324-325, 327-328, 334

analysis: 15, 24, 26, 32, 44, 49, 53, 72, 97, 104-105, 109, 139, 147, 186, 197, 205-206, 274, 351, 370, 397

apache: 302, 306, 311, 314-315, 321

append: 163

argument: 119, 127, 130-132, 134, 139-140, 154-155, 157, 159-161, 163, 168, 198, 231, 233, 259, 264, 266, 276, 293, 319, 357, 374

arrays: 127-128, 254, 258, 350, 370

artifacts: 61, 71, 73, 75, 82, 85, 88, 90, 96, 109, 146, 236, 355-356, 380, 383-384

assets: 351

attribute: 114, 201...

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