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.
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. Stochastic Optimization for Large-Scale Machine Learning available in PDF, EPUB and Kindle. "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. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning"--
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.
Iterative Stochastic Optimization for Large-scale Machine Learning and Statistical Inverse Problems
Iterative Stochastic Optimization for Large-scale Machine Learning and Statistical Inverse Problems - 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 Iterative Stochastic Optimization for Large-scale Machine Learning and Statistical Inverse Problems write by David A. Kozak. This book was released on 2020. Iterative Stochastic Optimization for Large-scale Machine Learning and Statistical Inverse Problems available in PDF, EPUB and Kindle.
First-order and Stochastic Optimization Methods for Machine Learning
First-order and Stochastic Optimization Methods 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 First-order and Stochastic Optimization Methods for Machine Learning write by Guanghui Lan. This book was released on 2020-05-15. First-order and Stochastic Optimization Methods for Machine Learning available in PDF, EPUB and Kindle. This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.
