284页pdf下载 深度之眼官方账号 编辑于 2024年04月24日 17:14 收录于文集 深度之眼产品专栏 · 35篇 今天来推荐一本Transformers宝典《Transformers for Machine Learning》,它现在在亚马逊上卖140美元。 全书共有60多种Transformer架构的讲解,还有相关的知识和技巧,不管你是搞语音、文本、时间序列还是计算机视觉的,...
关于Transformer的综合性书籍,市面上确实存在多本优秀的著作,它们从多个角度深入剖析了Transformer模型及其在不同领域的应用。以下是一些值得推荐的Transformer综合性书籍: 1. 《Transformers for Machine Learning》 内容概述:该书涵盖了60多个Transformer架构和对应的知识及技巧,涉及语音、文本、时间序列和计算机视觉等多个方...
《李宏毅深度学习教程》(李宏毅老师推荐👍,苹果书🍎),PDF下载地址:https://github.com/datawhalechina/leedl-tutorial/releases machine-learningtutorialreinforcement-learningdeep-learningcnntransformerganrnnpruningtransfer-learningbertdiffusionself-attentionnetwork-compressionchatgptleedl-tutorial ...
STABLE - Azure Machine Learning SDK for Python Search Python SDK overview Install or update Install or update SDK v2 Release notes Get support Tutorials & how-tos Sample Jupyter notebooks REST API reference CLI reference v.1 Reference Overview azureml.fsspec mltable azureml.accel.mo...
Refined Algorithm for Forecasting Technical Condition Index of a Transformer for Automating Maintenance and Repair Planning machine learningautomatic maintenance and repair planningCurrently, there is a tendency for planning the maintenance and repair of the equipment (MRO) based ... EN Kolobrodov,AA Vo...
Azure SDK for .NET-(ი)ს უკუკავშირი Azure SDK for .NET არის ღია წყაროს პროექტი. აირჩიეთ ბმული უკუკავშირის გასა...
STABLE - Azure Machine Learning SDK for Python 搜尋 Python SDK 概觀 安裝或更新 安裝或更新 SDK v2 版本資訊 取得支援 教學課程和操作說明 Jupyter Notebooks 範例 REST API 參考資料 CLI 參考 v.1 參考 概觀 azureml-fsspec mltable azureml-accel-models azureml-automl-core 概觀 azureml.au...
Machine learning-based generative models can generate novel molecules with desirable physiochemical and pharmacological properties from scratch. Many excellent generative models have been proposed, but multi-objective optimizations in molecular generative tasks are still quite challenging for most existing models...
We introduce Attention Free Transformer (AFT), an efficient variant of Transformers that eliminates the need for dot product self attention. In an AFT layer, the key and value are first combined with a set of learned position biases, the result of which is multiplied with the query in an el...
BERT 在英语 NLP 任务上的成功激励了它在其他语言中的应用。然而,只有对于拥有足够大量无标签数据的语言,才能使用 BERT 的训练流程。这促使了多语言模型的发展,希望通过在多种语言上进行预训练,模型能够将高资源语言的核心 NLP 知识转移到低资源语言中,最终得到一个能够在多语言之间对齐表示的多语言模型。 本章涵盖...