Machine learning in the aws cloud (Record no. 69133)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 04148cam a2200505Ia 4500 |
| 001 - CONTROL NUMBER | |
| control field | on1117320705 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220711203531.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 090713s2019 cau o 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119556725 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119556724 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119556749 |
| -- | (electronic bk. : oBook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119556740 |
| -- | (electronic bk. : oBook) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9781119556732 |
| -- | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 1119556732 |
| -- | (electronic bk.) |
| 029 1# - (OCLC) | |
| OCLC library identifier | AU@ |
| System control number | 000066121182 |
| 037 ## - | |
| -- | 327B574E-1719-44E0-A7E1-C0F710363365 |
| -- | OverDrive, Inc. |
| -- | http://www.overdrive.com |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.3/1 |
| 100 1# - AUTHOR NAME | |
| Author | Mishra, Abhishek. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning in the aws cloud |
| Sub Title | add intelligence to applications with amazon sagemaker and amazon rekognition / |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication | San Francisco, Calif. : |
| Publisher | Sybex, |
| Year of publication | 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 online resource |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Put the power of AWS Cloud machine learning services to work in your business and commercial applications! Machine Learning in the AWS Cloud introduces readers to the machine learning (ML) capabilities of the Amazon Web Services ecosystem and provides practical examples to solve real-world regression and classification problems. While readers do not need prior ML experience, they are expected to have some knowledge of Python and a basic knowledge of Amazon Web Services. Part One introduces readers to fundamental machine learning concepts. You will learn about the types of ML systems, how they are used, and challenges you may face with ML solutions. Part Two focuses on machine learning services provided by Amazon Web Services. You'll be introduced to the basics of cloud computing and AWS offerings in the cloud-based machine learning space. Then you'll learn to use Amazon Machine Learning to solve a simpler class of machine learning problems, and Amazon SageMaker to solve more complex problems. - Learn techniques that allow you to preprocess data, basic feature engineering, visualizing data, and model building - Discover common neural network frameworks with Amazon SageMaker - Solve computer vision problems with Amazon Rekognition - Benefit from illustrations, source code examples, and sidebars in each chapter The book appeals to both Python developers and technical/solution architects. Developers will find concrete examples that show them how to perform common ML tasks with Python on AWS. Technical/solution architects will find useful information on the machine learning capabilities of the AWS ecosystem. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Front Matter -- Fundamentals of Machine Learning. Introduction to Machine Learning -- Data Collection and Preprocessing -- Data Visualization with Python -- Creating Machine Learning Models with Scikit-learn -- Evaluating Machine Learning Models -- Machine Learning with Amazon Web Services. Introduction to Amazon Web Services -- AWS Global Infrastructure -- Identity and Access Management -- Amazon S3 -- Amazon Cognito -- Amazon DynamoDB -- AWS Lambda -- Amazon Comprehend -- Amazon Lex -- Amazon Machine Learning -- Amazon SageMaker -- Using Google TensorFlow with Amazon SageMaker -- Amazon Rekognition. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1002/9781119556749 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 588 ## - | |
| -- | Title from resource description page (Recorded Books, viewed September 02, 2019). |
| 610 27 - SUBJECT ADDED ENTRY--CORPORATE NAME | |
| -- | (OCoLC)fst01974501 |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Cloud computing. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | COMPUTERS / Machine Theory. |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Cloud computing. |
| -- | (OCoLC)fst01745899 |
| 650 #7 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| -- | (OCoLC)fst01004795 |
| 994 ## - | |
| -- | 92 |
| -- | DG1 |
No items available.