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1.XinmingWuteachingIn this course, we learn the basic concepts of artificial intelligence (AI) and its applications in Geosciences. The course coves 1) mathematic fundamentals of neural networks; 2) AI software platforms (Python, Jupyter, Tensorflow/Keras, cloud computing); 3) classic machine learning classifiers; http://cig.ustc.edu.cn/teaching/list.htm
2.2022机器学习好网站大收藏机器学习网站翻译各种外文书籍,与机器学习相关的目录主要有:数据科学、人工智能、datawhale等。 《ApacheCN 人工智能知识树》,《aiLearning》都是不错的学习材料模块。 【dataWhale】:http://www.datawhale.club/ Datawhale发展于2018年12月6日。 团队成员规模在不断扩大,有来自双非院校的优秀同学,也有来自上交、武大、清华等名校https://blog.csdn.net/ywj_1991/article/details/126950662
3.machinelearningmastery免费在线学习机器学习,从基础到高级Below is the 3 step process that you can use to get up-to-speed with statistical methods for machine learning, fast. Step 1: Discover what Statistical Methods are. What is Statistics (and why is it important in machine learning)? http://machinelearningmastery.com/start-here/
4.MachineLearningMastery官网,免费在线学习机器学习,从基础到高级MachineLearningMastery 免费在线学习机器学习,从基础到高级 免费在线学习机器学习,从基础到高级https://ai.itotii.com/sites/919.html
5.MachineLearningMasteryAI学习网站1345 337 0 上周最热排名:347 工具标签: # AI学习网站 直达网站 手机访问 工具描述 免费在线学习机器学习,从基础到高级https://www.aitop100.cn/tools/detail/1742.html
6.机器学习实战(MachineLearninginAction).pdf(最下方有相应链接) — 对于帮忙转发 MachineLearning(机器学习) 学习路线图 的 朋友,可以加群后私聊 瑶妹 企鹅 赠送 《机器学习实战》百度云 本文档使用 书栈(BookStack.CN) 构建 - 4 - 阅前必读 视频一套,谢谢 第一部分 分类 1.) 机器学习基础 机器学习实战-复习版(问题汇总) 2.) k-近邻算法 3.) https://m.book118.com/html/2022/0722/8133116143004121.shtm
7.Hicate/AiLearning:AiLearning:机器学习AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP - Hicate/AiLearninghttps://github.com/Hicate/AiLearning
8.JournalofMachineLearningResearchRLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous Control Jonas Eschmann, Dario Albani, Giuseppe Loianno, 2024. (Machine Learning Open Source Software Paper) [abs][pdf][bib] [code] White-Box Transformers via Sparse Rate Reduction: Compression Is All There Is? Yaodong https://www.jmlr.org/
9.二最新多智能体强化学习文章如何查阅{顶会:AAAIICML}13.最新多智能体强化学习方向论文 3.1 ICMLInternational Conference on Machine Learning [1]. Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning 作者: Shariq Iqbal (University of Southern California) · Christian Schroeder (University of Oxford) · Bei Peng (University of Oxford) ·https://blog.51cto.com/u_15485092/5032977
10.MachineLearningMastery官网,在线ai机器学习平台和资源库,从总的来说,Machine Learning Mastery是一家专注于机器学习和深度学习教育的在线平台,它通过提供综合的学习资源、实践导向的学习方法和实用的技术和应用,帮助学习者掌握机器学习技术并应用于实际问题。无论是初学者还是有一定经验的学习者,都可以从中获得有价值的学习和实践经验。 https://feizhuke.com/sites/machine-learning-mastery.html
11.MachineLearningSubjects: Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV) [610] arXiv:2412.10354 [pdf, html, other] A Library for Learning Neural Operators Jean Kossaifi, Nikola Kovachki, Zongyi Li, David Pitt, Miguel Liu-Schiaffini, Robert Joseph George, Boris Bonev, Kamyarhttp://arxiv.org/list/cs.LG/recent?skip=608&show=931
12.HomeWe currently maintain 673 datasets as a service to the machine learning community. Here, you can donate and find datasets used by millions of people all around the world! View DatasetsContribute a Dataset Popular Datasets Iris A small classic dataset from Fisher, 1936. One of the earliest knownhttp://archive.ics.uci.edu/
13.不要担心没数据!史上最全数据集网站汇总澎湃号·政务四.预测建模与机器学习数据集 1.UCIMachineLearningRepository ( https://archive.ics.uci.edu/ml/datasets.html ) UCI机器学习库显然是最著名的数据存储库。如果您正在寻找与机器学习存储库相关的数据集,通常是首选的地方。这些数据集包括了各种各样的数据集,从像Iris和泰坦尼克这样的流行数据集到最近的贡献,比如空https://www.thepaper.cn/newsDetail_forward_3853956
14.scikitlearn:machinelearninginPython—scikitApplications:Improved accuracy via parameter tuning. Algorithms:Grid search,cross validation,metrics, andmore Examples Preprocessing Feature extraction and normalization. Applications:Transforming input data such as text for use with machine learning algorithms. http://scikit-learn.org/stable/
15.这是一份超全机器学习&深度学习资源清单(105个AI站点),请收藏Distill(https://distill.pub/): 展示机器学习的最新文章 Google News(https://news.google.com/topics/CAAqIggKIhxDQkFTRHdvSkwyMHZNREZvZVdoZkVnSmxiaWdBUAE?hl=en-US&gl=US&ceid=US%3Aen): Google News Machine learning MIT News(http://news.mit.edu/topic/machine-learning): Machine learning | https://cloud.tencent.com/developer/article/1373217
16.应用机器学习的XGBoost简介·MachineLearningMastery博客原文:https://machinelearningmastery.com/gentle-introduction-xgboost-applied-machine-learning/ XGBoost 是一种算法库,近年来在应用机器学习和 Kaggle 竞赛中占据统治地位,它专长于处理结构化数据或表格数据。 XGBoost 是为速度和性能而设计的一种梯度提升决策树方法。 http://static.kancloud.cn/apachecn/ml-mastery-zh/1952511
17.DataScience,MachineLearning,AI&AnalyticsHow to Get Addicted to Machine Learning A simple guide for getting hooked to machine learning and building a successful career in the field. ByAbid Ali Awan, KDnuggets Assistant Editor on December 20, 2024 inMachine Learning How to Use Docker for Local Development Environments https://www.kdnuggets.com/
18.MachineLearningMastery——免费在线学习机器学习,从基础到高级免费在线学习机器学习,从基础到高级 网址:Start Here with Machine Learning (machinelearningmastery.com) https://home.designshidai.com/7815.html
19.MachineLearning(Theory)–MachinelearningandlearningMachine Learning (Theory) Machine learning and learning theory research Scroll down to content Posted on4/5/2023 An AI Miracle Malcontent The stark success of OpenAI’sGPT4 modelsurprised me shifting my view from “really good autocomplete” (roughly inline with intuitionshere) to a dialog agenthttp://www.hunch.net/
20.MachineLearning码农集市专业分享IT编程学习资源MachineLearningPt**ul 上传1.31MB 文件格式 zip 数据集很大,请从kaggle下载: 下载training_variants.zip和training_text.zip解压缩,并将这两个解压缩的文件放在同一文件夹的training文件夹中。 项目概况 它是多类(9类)分类问题,分类错误的成本很高。 KPI(关键绩效指标):多类对数丢失和混淆矩阵。 有3个功能: https://www.coder100.com/index/index/content/id/1132800
21.机器学习实战源代码(MachineLearninginAction)机器学习实战源代码及其详细解释 上传者:qq_51320133时间:2024-04-23 斯坦福2014机器学习课程源代码 Andrew Ng开源课程的Octave源码 上传者:hzm8341时间:2016-04-13 机器学习实战-官方git源代码3.x-machinelearninginaction-master 本人已学习,亲测可行。 https://www.iteye.com/resource/wshixinshouaaa-12521815
22.学习FinancialSignalProcessingandMachineLearning【金融信号处理与机器学习】 Financial Signal Processing and Machine Learning (16) 人大经济论坛-经管之家:分享大学、考研、论文、会计、留学、数据、经济学、金融学、管理学、统计学、博弈论、统计年鉴、行业分析包括等相关资源。 经管之家是国内活跃的在线教育咨询平台! https://bbs.pinggu.org/jg/kaoyankaobo_kaoyan_4987076_1.html
23.40个机器学习&深度学习最佳资源集合(书籍课程新闻博客论文5. Machine Learning for Trading 简介: 机器学习在交易中的应用 地址: https://www.udacity.com/course/machine-learning-for-trading--ud501 6. Oxford Deep NLP 简介: 牛津大学2017年开设的深度自然语言处理课程 地址: https://github.com/oxford-cs-deepnlp-2017/?ref=bestofml.com https://www.jiqizhixin.com/articles/2019-03-18-2
24.接近(几乎)任何机器学习问题(英文)301正式版.docApproaching(Almost)AnyMachineLearningProblemApproaching(Almost)AnyMachineLearningProblemISBN:978-82-692115-2-81Approaching(Almost)(namesinalphabeticalorder).AakashNainAdityaSoniAndreasMü(Almost)AnyMachineLearningProblemBeforeyoustart,.══════════════════════════════════https://www.taodocs.com/p-960581472.html