1.培训ChatGPT/GPT4科研应用论文写作数据分析与开发及AI绘图微软创始人比尔盖茨称chatGPT的出现有着重大历史意义,不亚于互联网和个人电脑的问世。360创始人周鸿祎认为未来各行各业如果不能搭上这班车,就有可能被淘汰。在AI2.0时代,AI思维已经成为一种必备的能力。因此,我们需要了解和掌握AI思维,https://mp.weixin.qq.com/s?__biz=MzA4NzI1NjE0Nw==&mid=2649813799&idx=2&sn=6bd60c89211bf3a9586ff279d403c61d&chksm=88386e1bbf4fe70db486d6bde737c8dfd990359435959120820ae2127086bdac90c94e024362&scene=27

2.AI时代的学习范式重构但是,随着 AI,特别是large language models的出现,这个认知框架正在经历一场革命性的转变。我们可以把这种转变称为learning paradigm shift,即学习范式的迁移。 让我们先从认知科学的角度来理解这个变化。根据Cognitive Load Theory认知负荷理论,人类的工作记忆容量是有限的。传统学习方式要求我们预先储存大量信息,这会导致https://zhuanlan.zhihu.com/p/10804850220

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4.OnInstrumentalVariableRegressionforDeepOfflinePolicy2. Background 2.1 Reinforcement learning and offline policy evaluation Reinforcement learning considers a Markov decision process S, A, P, R, μ0, γ , where S is the state space, A is the action space, and P (s |a, s) is the probability or probability density of making a https://dl.acm.org/doi/pdf/10.5555/3586589.3586891

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6.Oracle1 Oracle-Efficient Online Learning and Auction Design? MIROSLAV DUDíK, Microsoft Research, New York City NIKA HAGHTALAB, Cornell University HAIPENG LUO, University of Southern California ROBERT E. SCHAPIRE, Microsoft Research, New York City VASILIS SYRGKANIS, Microsoft Research, New England https://www.cs.cornell.edu/~nika/pubs/main-oracle-efficient.pdf?ref=hackernoon.com

7.eLearningArticlesDistanceLearningBookMOOCsandOpenPublicationShare is a Web site for sharing recent articles on e-learning. Most of them are free technical reports, electronic journal articles, and online books.https://www.publicationshare.com/

8.DesigninglearningandassessmentinadigitalageThe Heart of Worcestershire College’s Scheduled Online Learning and Assessment (SOLA) approach means the college met the FELTAG target in 2016 and is approaching 20% in a number of key areas. The SOLA model, which has been applied to all level 2 and 3 full-time programmes of study, ishttps://www.jisc.ac.uk/guides/designing-learning-and-assessment-in-a-digital-age

9.BackpropagationerrorfunctionlandscapeOnlinelearning is used for dynamic environments that provide a continuous stream of new training data patterns.Offlinelearning makes use of a training set of static patterns. Limitations[edit] Gradient descent can find the local minimum instead of the global minimum https://blog.csdn.net/omnispace/article/details/54754707

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11.Study:Physicalexerciseboostsmotorlearning—andmemory consolidation, and online + offline learning was used to quantify total learning. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.). Credit:Neurobiology of Learning and Memory(2023). DOI: 10.1016/j.nlm.https://medicalxpress.com/news/2024-01-physical-boosts-motor.html

12.SkillLearningand modeling capacity. However, existing methods for automatically learning temporally abstract world models usually require millions of online skill-conditioned world dynamics models purely from offline data, enabling sequences of skills to be rapidly planned to solve new tasks. Initialhttps://www.ri.cmu.edu/project/skill-learning-learning-skills-and-temporally-abstract-world-models-from-offline-demonstration/

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14.ParallelLearning:OverviewandPerspectiveforComputationalThe authors introduced a generalized Monte Carlo expectation maximization (GMCEM) for learning and gave an implementation by extending the generative adversarial network (GAN). In addition to these adversarial methods, some works treat the DA as optimization problem solved by searching algorithms. https://www.ieee-jas.net/article/doi/10.1109/JAS.2023.123375?viewType=HTML&pageType=en

15.LibraryforstreamingdataandincrementallearningInterface and pipelines that implement incremental models for both offline and online learning. A robust framework to construct an end-to- end AutoML pipeline with pipeline strategies that enables data ingestion, feature engineering, model selection, incremental training, and monitoring for data drifts https://github.com/IBM/sail

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17.DANCE:adeeplearninglibraryandbenchmarkplatformforWe also use optional cookies for advertising, personalisation of content, usage analysis, and social media. By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, withhttps://genomebiology.biomedcentral.com/articles/10.1186/s13059-024-03211-z

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24.如何评估机器学习模型(HowtoEvaluateMachineLearningModels本博文是对How to Evaluate Machine Learning Models这一博文的一个简单翻译和总结,文章主要从Evaluation Metrics ,Testing Mechanisms,Hyperparameter Tuning和A/B testing四个角度对机器学习模型的评价做了一一分析和讨论,建议有能力的人直接看原PO文。 1.评价指标(Evaluation Metrics ) https://blog.51cto.com/shanglianlm/5554438

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26.Adoptionofblendedlearning:ChineseuniversitystudentsAgainst the backdrop of the deep integration of the Internet with learning, blended learning offers the advantages of combining online and face-to-face learning to enrich the learning experience and improve knowledge management. Therefore, the objective https://www.nature.com/articles/s41599-023-01904-7

27.MobileLearningBrowse Mobile Learning content selected by the eLearning Learning community.http://www.elearninglearning.com/mobile-learning/

28.OfflineReinforcementLearningwithImplicitQOffline reinforcement learning requires reconciling two conflicting aims: learning a policy that improves over the behavior policy that collected the dataset, while at the same time minimizing the deviation from the behavior policy so as to avoid errors due to distributional shift. This trade-off ishttps://ui.adsabs.harvard.edu/abs/arXiv:2110.06169

29.ApplicationsofreinforcementlearninginenergysystemsDeep learning techniques and state-of-the-art actor-critic methods were not used by many articles. ? Batch reinforcement learning algorithms haveData-driven models, also known as black box control methods, use the knowledge derived by processing online or offline data instead of depending https://www.sciencedirect.com/science/article/pii/S1364032120309023