Stochastic Optimization for Large-scale Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Optimization for Large-scale Machine Learning write by Vinod Kumar Chauhan. This book was released on 2021-11-18. Stochastic Optimization for Large-scale Machine Learning available in PDF, EPUB and Kindle. Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Optimization for Machine Learning
Optimization for Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Optimization for Machine Learning write by Suvrit Sra. This book was released on 2012. Optimization for Machine Learning available in PDF, EPUB and Kindle. An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.
Large-Scale and Distributed Optimization
Large-Scale and Distributed Optimization - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large-Scale and Distributed Optimization write by Pontus Giselsson. This book was released on 2018-11-11. Large-Scale and Distributed Optimization available in PDF, EPUB and Kindle. This book presents tools and methods for large-scale and distributed optimization. Since many methods in "Big Data" fields rely on solving large-scale optimization problems, often in distributed fashion, this topic has over the last decade emerged to become very important. As well as specific coverage of this active research field, the book serves as a powerful source of information for practitioners as well as theoreticians. Large-Scale and Distributed Optimization is a unique combination of contributions from leading experts in the field, who were speakers at the LCCC Focus Period on Large-Scale and Distributed Optimization, held in Lund, 14th–16th June 2017. A source of information and innovative ideas for current and future research, this book will appeal to researchers, academics, and students who are interested in large-scale optimization.
Stochastic Optimization for Large-scale Machine Learning
Stochastic Optimization for Large-scale Machine Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Stochastic Optimization for Large-scale Machine Learning write by Vinod Kumar Chauhan. This book was released on 2021-11-18. Stochastic Optimization for Large-scale Machine Learning available in PDF, EPUB and Kindle. Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.
Large Scale Optimization for Deep Learning
Large Scale Optimization for Deep Learning - read free eBook in online reader or directly download on the web page. Select files or add your book in reader. Download and read online ebook Large Scale Optimization for Deep Learning write by Xiangru Lian. This book was released on 2019. Large Scale Optimization for Deep Learning available in PDF, EPUB and Kindle. "In the big data era, deep learning is often employed to solve all kinds of problems from traditional classification to reinforcement learning. It often takes weeks or even months to train and tune parameters for a deep neural network. Therefore, the efficiency turns out to be a key bottleneck of deep learning. Parallel optimization has then emerged as an essential technology to solve computationally intensive problems. How to design efficient parallel systems and convergent algorithms becomes more and more important. In this dissertation we investigate how to improve the optimization for deep learning from the following aspects: 1. Asynchronous parallelism for reducing the synchronization overhead in parallel computation. 2. Decentralized parallelism to make parallel algorithms more feasible and robust to network topology, latency, and bandwidth. 3. Lossy compression in communication with error compensation for reducing the communication cost without sacrificing the model's quality. 4. Compositional optimization, where the objective function is composed of multiple expectation of loss functions. Batch normalization can be formulated as a kind of compositional optimization. We provide convergence analysis for all the algorithms we propose, and show when we should and should not use them"--Page vii.
