装帧:Paperback ISBN:9781098115784 豆瓣评分 7.5 23人评价 5星 30.4% 4星 34.8% 3星 30.4% 2星 4.3% 1星 0.0% 评价: 写笔记 写书评 加入购书单 分享到 推荐 内容简介· ··· The design patterns in this book capture best practices and solutions to recurring problems in machine learning. Authors...
这里就没什么好多说的,其实就是用于训练,验证,测试的数据,以及拿到数后能够衍生出来的特征 机器学习的过程(The Machine Learning Process) 第一步是训练模型(the process of passing training data to a model so that it can learn to identify patterns in data) 测试模型在验证集上的效果,This is known as ...
1. The Need for Machine Learning Design Patterns 2. Data Representation Design Patterns 3. Problem Representation Design Patterns 4. Model Training Patterns 5. Design Patterns for Resilient Serving 6. Reproducibility Design Patterns 7. Responsible AI 8. Connected Patterns Index 【干货书】机器学习设计...
Chapter 4. Model Training Patterns Machine learning models are usually trained iteratively, and this iterative process is informally called the training loop. In this chapter, we discuss what the … - Selection from Machine Learning Design Patterns [Boo
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She teaches CS 329S: Machine Learning Systems Design at Stanford, whose lecture notes this book is based on. LinkedI... (展开全部) 喜欢读"Designing Machine Learning Systems"的人也喜欢 ··· Architecting for Scale, 2nd Edition Machine Learning Design Patterns 7.5 Deep Learning with Python,...
Today, we look at software as a fusion of traditional code, machine learning models and the underlying data. Design patterns, however, have not been extended yet to deal with the challenges of this new era. In Software 2.0 common challenges do not appear only at the code level but at the...
Machine learning gives computers the power of tacit knowledge that allows these machines to make connections, discover patterns and make predictions based on what it learned in the past. Machine learning’s use of tacit knowledge has made it a go-to technology for almost every industry from fin...
(If you like this, you may also be interested in a previous post on design patterns in ML code, largely inspired by the classic Gang of Four book.)Process Raw Data Only Once: To reduce redundancyA key pattern when designing data pipelines is to process and aggregate raw data just once,...
ML pipeline design has undergone several evolutions in the past decade with advances in memory and processor performance, storage systems, and the increasing scale of data sets. We describe how these design patterns changed, what processes they went thro