SØG - mellem flere end 8 millioner bøger:
Viser: Machine Learning in Production - Developing and Optimizing Data Science Workflows and Applications
Machine Learning in Production Vital Source e-bog
Andrew Kelleher og Adam Kelleher
(2019)
Machine Learning in Production Vital Source e-bog
Andrew Kelleher og Adam Kelleher
(2019)
Machine Learning in Production Vital Source e-bog
Andrew Kelleher og Adam Kelleher
(2019)
Machine Learning in Production
Developing and Optimizing Data Science Workflows and Applications
Andrew Kelleher og Adam Kelleher
(2019)
Sprog: Engelsk
om ca. 10 hverdage
Detaljer om varen
- 1. Udgave
- Vital Source 90 day rentals (dynamic pages)
- Udgiver: Pearson International (Februar 2019)
- Forfattere: Andrew Kelleher og Adam Kelleher
- ISBN: 9780134116563R90
Bookshelf online: 90 dage fra købsdato.
Bookshelf appen: 90 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Vital Source 180 day rentals (dynamic pages)
- Udgiver: Pearson International (Februar 2019)
- Forfattere: Andrew Kelleher og Adam Kelleher
- ISBN: 9780134116563R180
Bookshelf online: 180 dage fra købsdato.
Bookshelf appen: 180 dage fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Vital Source 365 day rentals (dynamic pages)
- Udgiver: Pearson International (Februar 2019)
- Forfattere: Andrew Kelleher og Adam Kelleher
- ISBN: 9780134116563R365
Bookshelf online: 5 år fra købsdato.
Bookshelf appen: 5 år fra købsdato.
Udgiveren oplyser at følgende begrænsninger er gældende for dette produkt:
Print: 2 sider kan printes ad gangen
Copy: højest 2 sider i alt kan kopieres (copy/paste)
Detaljer om varen
- 1. Udgave
- Paperback: 288 sider
- Udgiver: Pearson Education, Limited (Maj 2019)
- Forfattere: Andrew Kelleher og Adam Kelleher
- ISBN: 9780134116549
The typical data science task in industry starts with an "ask" from the business. But few data scientists have been taught what to do with that ask. This book shows them how to assess it in the context of the business's goals, reframe it to work optimally for both the data scientist and the employer, and then execute on it. Written by two of the experts who've achieved breakthrough optimizations at BuzzFeed, it's packed with real-world examples that take you from start to finish: from ask to actionable insight.
Andrew Kelleher and Adam Kelleher walk you through well-formed, concrete principles for approaching common data science problems, giving you an easy-to-use checklist for effective execution. Using their principles and techniques, you'll gain deeper understanding of your data, learn how to analyze noise and confounding variables so they don't compromise your analysis, and save weeks of iterative improvement by planning your projects more effectively upfront.
Once you've mastered their principles, you'll put them to work in two realistic, beginning-to-end site optimization tasks. These extended examples come complete with reusable code examples and recommended open-source solutions designed for easy adaptation to your everyday challenges. They will be especially valuable for anyone seeking their first data science job -- and everyone who's found that job and wants to succeed in it.
Part I: Principles of Framing
Chapter 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 Nodes Bibliography