| 000 | 01292nam a22001817a 4500 | ||
|---|---|---|---|
| 003 | OSt | ||
| 005 | 20250606165437.0 | ||
| 008 | 250606b |||||||| |||| 00| 0 eng d | ||
| 020 | _a9789389588507 | ||
| 082 |
_a006.31 _bKEL |
||
| 100 | _aKelleher, Andrew | ||
| 245 | _aMachine Learning in Production | ||
| 260 |
_aNoida _bPearson _c2025 |
||
| 300 |
_axxiv, 229p. _c24 x 18 cm |
||
| 520 | _ahapter 1: The Role of the Data Scientist Chapter 2: Project Workflow Chapter 3: Quantifying Error Chapter 4: Data Encoding and Preprocessing Chapter 5: Hypothesis Testing Chapter 6: Data Visualization Part II: Algorithms and Architectures Chapter 7: Introduction to Algorithms and Architectures Chapter 8: Comparison Chapter 9: Regression Chapter 10: Classification and Clustering Chapter 11: Bayesian Networks Chapter 12: Dimensional Reduction and Latent Variable Models Chapter 13: Causal Inference Chapter 14: Advanced Machine Learning Part III: Bottlenecks and Optimizations Chapter 15: Hardware Fundamentals Chapter 16: Software Fundamentals Chapter 17: Software Architecture Chapter 18: The CAP Theorem Chapter 19: Logical Network Topological Node | ||
| 700 | _aKelleher, Adam | ||
| 942 |
_2ddc _cREF |
||
| 999 |
_c6418 _d6418 |
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