Machine learning for hackers / (Record no. 22684)
[ view plain ]
| 000 -LEADER | |
|---|---|
| fixed length control field | 03956cam a22003617a 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 130227s2012 caua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER | |
| LC control number | 2012277057 |
| 016 7# - NATIONAL BIBLIOGRAPHIC AGENCY CONTROL NUMBER | |
| Record control number | 015952116 |
| Source | Uk |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789350236741 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 1449303714 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)ocn783384312 |
| 042 ## - AUTHENTICATION CODE | |
| Authentication code | lccopycat |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.1 MAC-C |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Conway, Drew. |
| 245 10 - TITLE STATEMENT | |
| Title | Machine learning for hackers / |
| Statement of responsibility, etc | Drew Conway and John Myles White. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Place of publication, distribution, etc | Sebastopol, CA : |
| Name of publisher, distributor, etc | O'Reilly Media, |
| Date of publication, distribution, etc | 2012. |
| 500 ## - GENERAL NOTE | |
| General note | "Case studies and algorithms to get you started"--Cover. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references (p. 293-294) and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Machine generated contents note: 1. Using R -- R for Machine Learning -- Downloading and Installing R -- IDEs and Text Editors -- Loading and Installing R Packages -- R Basics for Machine Learning -- Further Reading on R -- 2. Data Exploration -- Exploration versus Confirmation -- What Is Data? -- Inferring the Types of Columns in Your Data -- Inferring Meaning -- Numeric Summaries -- Means, Medians, and Modes -- Quantiles -- Standard Deviations and Variances -- Exploratory Data Visualization -- Visualizing the Relationships Between Columns -- 3. Classification: Spam Filtering -- This or That: Binary Classification -- Moving Gently into Conditional Probability -- Writing Our First Bayesian Spam Classifier -- Defining the Classifier and Testing It with Hard Ham -- Testing the Classifier Against All Email Types -- Improving the Results -- 4. Ranking: Priority Inbox -- How Do You Sort Something When You Don't Know the Order? -- Ordering Email Messages by Priority. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Contents note continued: Priority Features of Email -- Writing a Priority Inbox -- Functions for Extracting the Feature Set -- Creating a Weighting Scheme for Ranking -- Weighting from Email Thread Activity -- Training and Testing the Ranker -- 5. Regression: Predicting Page Views -- Introducing Regression -- The Baseline Model -- Regression Using Dummy Variables -- Linear Regression in a Nutshell -- Predicting Web Traffic -- Defining Correlation -- 6. Regularization: Text Regression -- Nonlinear Relationships Between Columns: Beyond Straight Lines -- Introducing Polynomial Regression -- Methods for Preventing Overfitting -- Preventing Overfitting with Regularization -- Text Regression -- Logistic Regression to the Rescue -- 7. Optimization: Breaking Codes -- Introduction to Optimization -- Ridge Regression -- Code Breaking as Optimization -- 8. PCA: Building a Market Index -- Unsupervised Learning -- 9. MDS: Visually Exploring US Senator Similarity. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Contents note continued: Clustering Based on Similarity -- A Brief Introduction to Distance Metrics and Multidirectional Scaling -- How Do US Senators Cluster? -- Analyzing US Senator Roll Call Data (101st--111th Congresses) -- 10. kNN: Recommendation Systems -- The k-Nearest Neighbors Algorithm -- R Package Installation Data -- 11. Analyzing Social Graphs -- Social Network Analysis -- Thinking Graphically -- Hacking Twitter Social Graph Data -- Working with the Google SocialGraph API -- Analyzing Twitter Networks -- Local Community Structure -- Visualizing the Clustered Twitter Network with Gephi -- Building Your Own "Who to Follow" Engine -- 12. Model Comparison -- SVMs: The Support Vector Machine -- Comparing Algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Computer algorithms. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Electronic data processing |
| General subdivision | Automation. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | White, John Myles. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://www.loc.gov/catdir/enhancements/fy1307/2012277057-b.html |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://www.loc.gov/catdir/enhancements/fy1307/2012277057-d.html |
| 856 41 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | http://www.loc.gov/catdir/enhancements/fy1307/2012277057-t.html |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Item type | TEQIP Reference |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) | |
| a | 7 |
| b | cbc |
| c | copycat |
| d | 2 |
| e | ncip |
| f | 20 |
| g | y-gencatlg |
| Withdrawn status | Lost status | Damaged status | Home library | Current library | Shelving location | Date acquired | Cost, normal purchase price | Full call number | Barcode | Date last seen | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|
| CENTRAL LIBRARY | CENTRAL LIBRARY | Reference | 07/06/2014 | 525.00 | 005.1 MAC-C | T1813 | 07/06/2014 | TEQIP Reference |