| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046040774 | |
| 005 | 20251107160104 | |
| 008 | 200803s2020 caua 001 0 eng d | |
| 015 | ▼a GBC078865 ▼2 bnb | |
| 020 | ▼a 9781492072744 | |
| 020 | ▼a 1492072745 | |
| 035 | ▼a (OCoLC)1164815296 | |
| 040 | ▼a SRB ▼b eng ▼e rda ▼c SRB ▼d UKMGB ▼d KUB ▼d 211009 | |
| 082 | 0 4 | ▼a 005.74 ▼2 23 |
| 084 | ▼a 005.74 ▼2 DDCK | |
| 090 | ▼a 005.74 ▼b E38p | |
| 100 | 1 | ▼a El Emam, Khaled ▼0 AUTH(211009)175274. |
| 245 | 1 0 | ▼a Practical synthetic data generation : ▼b balancing privacy and the broad availability of data / ▼c Khaled El Emam, Lucy Mosquera, and Richard Hoptroff. |
| 260 | ▼a Sebastopol, CA : ▼b O'Reilly Media, Inc., ▼c c2020. | |
| 300 | ▼a ix, 151 p. : ▼b ill. ; ▼c 24 cm. | |
| 500 | ▼a Includes index. | |
| 520 | ▼a Building and testing machine learning models requires access to large and diverse data. But where can you find usable datasets without running into privacy issues? This practical book introduces techniques for generating synthetic data--fake data generated from real data--so you can perform secondary analysis to do research, understand customer behaviors, develop new products, or generate new revenue. Data scientists will learn how synthetic data generation provides a way to make such data broadly available for secondary purposes while addressing many privacy concerns. Analysts will learn the principles and steps for generating synthetic data from real datasets. And business leaders will see how synthetic data can help accelerate time to a product or solution. | |
| 650 | 0 | ▼a Electronic data processing. |
| 650 | 0 | ▼a Data mining. |
| 650 | 0 | ▼a Databases. |
| 650 | 0 | ▼a Computer simulation. |
| 700 | 1 | ▼a Mosquera, Lucy. |
| 700 | 1 | ▼a Hoptroff, Richard ▼0 AUTH(211009)175277. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 중앙도서관/서고6층/ | 청구기호 005.74 E38p | 등록번호 111831713 (1회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |