Title
Scaling Up Machine Learning: Parallel And Distributed Approaches
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This Book Presents An Integrated Collection Of Representative Approaches For Scaling Up Machine Learning And Data Mining Methods On Parallel And Distributed Computing Platforms. Demand For Parallelizing Learning Algorithms Is Highly Taskspecific: In Some Settings It Is Driven By The Enormous Dataset Sizes, In Others By Model Complexity Or By Realtime Performance Requirements. Making Taskappropriate Algorithm And Platform Choices For Largescale Machine Learning Requires Understanding The Benefits, Tradeoffs, And Constraints Of The Available Options. Solutions Presented In The Book Cover A Range Of Parallelization Platforms From Fpgas And Gpus To Multicore Systems And Commodity Clusters, Concurrent Programming Frameworks Including Cuda, Mpi, Mapreduce, And Dryadlinq, And Learning Settings (Supervised, Unsupervised, Semisupervised, And Online Learning). Extensive Coverage Of Parallelization Of Boosted Trees, Svms, Spectral Clustering, Belief Propagation And Other Popular Learning Algorithms And Deep Dives Into Several Applications Make The Book Equally Useful For Researchers, Students, And Practitioners.
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- Q: How many pages does the book have? A: This book contains four hundred ninety-two pages. It provides a comprehensive exploration of scaling up machine learning approaches.
- Q: What is the binding type of this book? A: This book is hardcover. The hardcover binding enhances durability, making it suitable for frequent use.
- Q: What are the dimensions of the book? A: The book measures seven point five two inches in length, one point five inches in width, and ten point two four inches in height.
- Q: Who is the author of the book? A: The author of this book is Ron Bekkerman. He is known for his expertise in machine learning and data science.
- Q: What is the genre of this book? A: This book falls under the category of computer vision and pattern recognition. It focuses on advanced machine learning techniques.
- Q: Is this book suitable for beginners in machine learning? A: Yes, this book is suitable for beginners. It covers fundamental concepts before discussing more advanced parallel and distributed approaches.
- Q: Can this book be used as a textbook for a course? A: Yes, this book is ideal for academic use. Its structured content makes it suitable for university-level courses in machine learning.
- Q: Is there a specific reading level for this book? A: This book is aimed at readers with a basic understanding of machine learning concepts. It is suitable for graduate students and professionals.
- Q: How do I store this book to keep it in good condition? A: Store this book upright on a shelf away from direct sunlight. This helps prevent damage to the binding and pages.
- Q: What is the best way to clean this hardcover book? A: To clean this hardcover book, use a dry microfiber cloth. Avoid using water or cleaners that may damage the cover.
- Q: What should I do if the book arrives damaged? A: If the book arrives damaged, contact the seller for return or exchange options. Most sellers provide a satisfaction guarantee.
- Q: Is there a warranty for this book? A: No, books typically do not come with a warranty. However, check with the seller for their return policy.
- Q: How can I access additional resources related to this book? A: Check the publisher's website for supplemental materials. Often, books like this have additional resources for deeper learning.
- Q: Are there any companion materials for this book? A: Yes, there may be companion materials available such as online lectures or exercises. These can enhance understanding of the content.
- Q: Is this book widely recommended by professionals in the field? A: Yes, many professionals recommend this book for its in-depth analysis and practical applications of machine learning.