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by Adam Kelleher, Andrew Kelleher
Machine Learning in Production: Developing and Optimizing Data Science Workflows and Applications, First Edition
Cover
About This E-Book
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Who This Book Is For
What This Book Covers
Going Forward
About the Authors
I: Principles of Framing
1. The Role of the Data Scientist
1.1 Introduction
1.2 The Role of the Data Scientist
1.3 Conclusion
2. Project Workflow
2.1 Introduction
2.2 The Data Team Context
2.3 Agile Development and the Product Focus
2.4 Conclusion
3. Quantifying Error
3.1 Introduction
3.2 Quantifying Error in Measured Values
3.3 Sampling Error
3.4 Error Propagation
3.5 Conclusion
4. Data Encoding and Preprocessing
4.1 Introduction
4.2 Simple Text Preprocessing
4.3 Information Loss
4.4 Conclusion
5. Hypothesis Testing
5.1 Introduction
5.2 What Is a Hypothesis?
5.3 Types of Errors
5.4 P-values and Confidence Intervals
5.5 Multiple Testing and “P-hacking”
5.6 An Example
5.7 Planning and Context
5.8 Conclusion
6. Data Visualization
6.1 Introduction
6.2 Distributions and Summary Statistics
6.3 Time-Series Plots
6.4 Graph Visualization
6.5 Conclusion
II: Algorithms and Architectures
7. Introduction to Algorithms and Architectures
7.1 Introduction
7.2 Architectures
7.3 Models
7.4 Conclusion
8. Comparison
8.1 Introduction
8.2 Jaccard Distance
8.3 MinHash
8.4 Cosine Similarity
8.5 Mahalanobis Distance
8.6 Conclusion
9. Regression
9.1 Introduction
9.2 Linear Least Squares
9.3 Nonlinear Regression with Linear Regression
9.4 Random Forest
9.5 Conclusion
10. Classification and Clustering
10.1 Introduction
10.2 Logistic Regression
10.3 Bayesian Inference, Naive Bayes
10.4 K-Means
10.5 Leading Eigenvalue
10.6 Greedy Louvain
10.7 Nearest Neighbors
10.8 Conclusion
11. Bayesian Networks
11.1 Introduction
11.2 Causal Graphs, Conditional Independence, and Markovity
11.3 D-separation and the Markov Property
11.4 Causal Graphs as Bayesian Networks
11.5 Fitting Models
11.6 Conclusion
12. Dimensional Reduction and Latent Variable Models
12.1 Introduction
12.2 Priors
12.3 Factor Analysis
12.4 Principal Components Analysis
12.5 Independent Component Analysis
12.6 Latent Dirichlet Allocation
12.7 Conclusion
13. Causal Inference
13.1 Introduction
13.2 Experiments
13.3 Observation: An Example
13.4 Controlling to Block Non-causal Paths
13.5 Machine-Learning Estimators
13.6 Conclusion
14. Advanced Machine Learning
14.1 Introduction
14.2 Optimization
14.3 Neural Networks
14.4 Conclusion
III: Bottlenecks and Optimizations
15. Hardware Fundamentals
15.1 Introduction
15.2 Random Access Memory
15.3 Nonvolatile/Persistent Storage
15.4 Throughput
15.5 Processors
15.6 Conclusion
16. Software Fundamentals
16.1 Introduction
16.2 Paging
16.3 Indexing
16.4 Granularity
16.5 Robustness
16.6 Extract, Transfer/Transform, Load
16.7 Conclusion
17. Software Architecture
17.1 Introduction
17.2 Client-Server Architecture
17.3 N-tier/Service-Oriented Architecture
17.4 Microservices
17.5 Monolith
17.6 Practical Cases (Mix-and-Match Architectures)
17.7 Conclusion
18. The CAP Theorem
18.1 Introduction
18.2 Consistency/Concurrency
18.3 Availability
18.4 Partition Tolerance
18.5 Conclusion
19. Logical Network Topological Nodes
19.1 Introduction
19.2 Network Diagrams
19.3 Load Balancing
19.4 Caches
19.5 Databases
19.6 Queues
19.7 Conclusion
Bibliography
Index
Credits
Code Snippets
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