Distributed Machine Learning and Gradient Optimization (2022) [Jiang et al] [9789811634192]

• Tongue: English
• Leaves: 179
• Category: Library

✦ Table of Contents

Preface
Acknowledgments
Contents
Acronyms
1 Introduction
1.1 Background
1.1.1 Methodology of Machine Learning
1.1.2 Machine Learning Meets Big Data
1.2 Distributed Machine Learning
1.3 Gradient Optimization
1.3.1 First-Order Gradient Optimization Algorithms
1.3.1.1 Batch Gradient Descent
1.3.1.2 Stochastic Gradient Descent
1.3.1.3 Minibatch Gradient Descent
1.3.2 Serial Gradient Optimization
1.3.3 Distributed Gradient Optimization
1.4 Open Problems
References
2 Basics of Distributed Machine Learning
2.1 Anatomy of Distributed Machine Learning
2.2 Parallelism
2.2.1 Data Parallelism
2.2.1.1 Horizontal Partitioning
2.2.1.2 Vertical Partitioning
2.2.2 Model Parallelism
2.2.3 Hybrid Parallelism
2.3 Parameter Sharing
2.3.1 Shared-Nothing
2.3.1.1 Message Passing Interface
2.3.1.2 Remote Procedure Call
2.3.1.3 MapReduce
2.3.2 Shared-Memory
2.4 Synchronization
2.4.1 Bulk Synchronous Protocol
2.4.2 Asynchronous Protocol
2.4.3 Stale Synchronous Protocol
2.5 Communication Optimization
2.5.1 Lower Numerical Precision
2.5.2 Communication Compression
2.5.2.1 Lossless Compression for Integer Numbers
2.5.2.2 Lossless Compression for Sparse Matrices
2.5.2.3 Lossy Compression for Floating-point Numbers
References
3 Distributed Gradient Optimization Algorithms
3.1 Linear Models
3.1.1 Formalization of Linear Models
3.1.2 Overview of Popular Linear Models
3.1.3 Single-Node Gradient Optimization
3.1.3.1 Serial Gradient Optimization
3.1.3.2 Single-Node Parallel Gradient Optimization
3.1.4 Distributed Gradient Optimization
3.1.4.1 MR-BSP-SGD
3.1.4.2 MR-MA-SGD
3.1.4.3 PS-BSP-SGD
3.1.4.4 PS-SSP-SGD
3.1.4.5 Column-SGD
3.1.4.6 Other Related works
3.2 Neural Network Models
3.2.1 Formalization of Neural Network
3.2.1.1 Model Definition
3.2.1.2 Back-Propagation
3.2.2 Overview of Popular Neural Network Models
3.2.2.1 AutoEncoder
3.2.2.2 Deep Belief Network
3.2.2.3 Convolutional Neural Network
3.2.2.4 Recurrent Neural Network
3.2.2.5 Other Neural Networks
3.2.3 Distributed Gradient Optimization
3.2.3.1 PS-ASP-SGD
3.2.3.2 Decentralized-PSGD
3.2.3.3 Decentralized-ASP-SGD
3.2.3.4 QSGD
3.2.3.5 Sparsification-SGD
3.2.3.6 Model-Parallel SGD
3.3 Gradient Boosting Decision Tree
3.3.1 Formalization of Gradient Boosting Decision Tree
3.3.2 Distributed Gradient Optimization
References
4 Distributed Machine Learning Systems
4.1 General Machine Learning Systems
4.1.1 MapReduce Systems
4.1.2 Parameter Server Systems
4.2 Specialized Machine Learning Systems
4.3 Deep Learning Systems
4.4 Cloud Machine Learning Systems
4.4.1 Geo-Distributed Systems
4.4.2 Serverless Systems
4.5 In-Database Machine Learning Systems
References
5 Conclusion
5.1 Summary of the Book
5.2 Further Reading
References