Description
| Product ID: | 9781032131757 |
| Product Form: | Hardback |
| Country of Manufacture: | GB |
| Title: | Stochastic Optimization for Large-scale Machine Learning |
| Authors: | Author: Vinod Kumar Chauhan |
| Page Count: | 158 |
| Subjects: | Algorithms and data structures, Algorithms & data structures, Programming and scripting languages: general, Neural networks and fuzzy systems, Programming & scripting languages: general, Neural networks & fuzzy systems |
| Description: | Select Guide Rating 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. 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:
The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning. |
| Imprint Name: | CRC Press |
| Publisher Name: | Taylor & Francis Ltd |
| Country of Publication: | GB |
| Publishing Date: | 2021-11-19 |
