March 2023
Intermediate to advanced
291 pages
8h 54m
English
Content preview from Scaling Machine Learning with SparkBecome an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.
Index
A
- accelerated failure time (AFT), Regression
- accelerator-aware scheduling, Accelerator-Aware Scheduling-Accelerator-Aware Scheduling
- accelerators, AI, Resource Management, TensorFlow Distributed Machine Learning Approach
- actions, DataFrames are immutable, Binary format
- activation functions, Bridging Spark and Deep Learning Frameworks
- Adam (Adaptive Movement Estimation) optimizer, PyTorch Mechanics and Concepts
- adaptive bucketing of data, Structuring continuous numerical data
- AFT (accelerated failure time), Regression
- AFTSurvivalRegression, Regression
- aggregation, Types of Ensemble Methods-Decentralized decision trees, MultiWorkerMirroredStrategy: Multiple Machines, Synchronous, MultiWorkerMirroredStrategy: Multiple Machines, Synchronous
- AI accelerators, TensorFlow Distributed Machine Learning Approach
- algorithms, MLlib (see training the model)
- all-reduce algorithm, MirroredStrategy: One Machine, Multiple Processors, Local Copy, MultiWorkerMirroredStrategy: Multiple Machines, Synchronous, MultiWorkerMirroredStrategy: Multiple Machines, Synchronous-MultiWorkerMirroredStrategy: Multiple Machines, Synchronous, Collective communication in PyTorch, Collective communication in PyTorch, Collective communication in PyTorch
- all_gather function, Collective communication in PyTorch
- all_gather, PyTorch, Collective communication in PyTorch, Collective communication in PyTorch
- all_reduce function, Collective communication in PyTorch
- alternating least squares (ALS), ALS for collaborative filtering
- annotations, ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.