If the problem of machine learning is posed as one of neural net optimization [5, 191, a precise scientific context is established in which to explore questions such as generalization.A synthetic neural net is a particular kind of circuit parameterized by real-valued connec...
with a larger value and providing longer fine-tuning text. In this work, we first observe that fine-tuning a RoPE-based LLM with either a smaller or larger base in pre-training context length could significantly enhance its extrapolation performance. After that, we propose \textbf{\textit{Scal...
副标题:Distributed ML with MLlib, TensorFlow, and PyTorch 出版年:2023-4-1 页数:291 定价:USD 77.89 装帧:Paperback ISBN:9781098106829 豆瓣评分 评价人数不足 评价: 写笔记 写书评 加入购书单 分享到 推荐 内容简介· ··· Learn how to build end-to-end scalable machine learning solutions with Apac...
Training complex machine learning models in parallel is an increasingly important workload. We accelerate distributed parallel training by designing a communication primitive that uses a programmable switch dataplane to execute a key step of the training process. Our approach, SwitchML, red...
The renaissance of machine learning (ML) and deep learning (DL) over the last decade is accompanied by an unscalable computational cost, limiting its advancement and weighing on the field in practice. In this thesis we take a systematic approach to address the algorithmic and methodological ...
Scaling Machine Learning with TensorFlow Jeff Dean Google Brain team g.co/brain Presenting the work of many people at Google Our Mission: Make Machines Intelligent. Improve People's Lives. Google Brain Team: Research Impact ● Since 2012, published > 130 papers at top venues in machine learning...
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 task-specific: in some settings it is driven by the enormous dataset size...
本期论文:Scaling Distributed Machine Learning with the Parameter Server 背景 参数服务器是一种编程框架,用于简化分布式机器学习程序的编写,其中重点在于对大规模参数的分布式存储和协同的支持。 机器学习任务相比于其他计算任务而言,具有以下特点: 迭代性:模型的更新并非一次完成,需要多次迭代 容错性:即使在每次迭代中...
Dealing with floating-point numbers 定点数与浮点数的表示方法 论文中的方法 Scaling Distributed Machine Learning with In-Network Aggregation 摘要 此片论文主要设计了一种交换机SwitchML,通过将网络上多个worker的模型更新在交换机中aggregate来降低数据传递来的巨大的开销。
You are most likely familiar with the phrase “garbage in, garbage out.” It captures well the notion that flawed, incorrect, or nonsensical data input will always produce faulty output. In the context of machine learning, it also emphasizes the fact that the attention we devote to ingesting...