Title
Scaling Up Machine Learning: Parallel And Distributed Approaches
Delivery time: 8-12 business days (International)
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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⚠️ WARNING (California Proposition 65):
This product may contain chemicals known to the State of California to cause cancer, birth defects, or other reproductive harm.
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Shipping & Returns
Shipping
We ship your order within 2–3 business days for USA deliveries and 5–8 business days for international shipments. Once your package has been dispatched from our warehouse, you'll receive an email confirmation with a tracking number, allowing you to track the status of your delivery.
Returns
To facilitate a smooth return process, a Return Authorization (RA) Number is required for all returns. Returns without a valid RA number will be declined and may incur additional fees. You can request an RA number within 15 days of the original delivery date. For more details, please refer to our Return & Refund Policy page.
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Warranty
We provide a 2-year limited warranty, from the date of purchase for all our products.
If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.
This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).
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Frequently Asked Questions
- Q: What is the main focus of 'Scaling up Machine Learning'? A: The book primarily focuses on parallel and distributed approaches to machine learning, providing insights into scaling algorithms and systems.
- Q: Who is the author of this book? A: The author of 'Scaling up Machine Learning' is Ron Bekkerman.
- Q: How many pages does the book contain? A: The book contains 492 pages.
- Q: What type of binding does this book have? A: This book is available in hardcover binding.
- Q: When was 'Scaling up Machine Learning' published? A: The book was published on December 30, 2011.
- Q: Is this book suitable for beginners in machine learning? A: While the book offers valuable insights, it is recommended for readers with some background in machine learning concepts.
- Q: Are there any specific prerequisites to understand the content of this book? A: A foundational understanding of machine learning and programming concepts will be beneficial for readers.
- Q: What edition of the book is available? A: This is the first edition of 'Scaling up Machine Learning'.
- Q: Does the book include practical examples or case studies? A: Yes, the book includes practical examples and case studies to illustrate the concepts discussed.
- Q: Is this book relevant for current machine learning technologies? A: Yes, the book discusses foundational principles that are applicable to many current machine learning technologies and practices.