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第4行： |
| | *小清爱提问：监督学习和无监督学习有什么不同？[] | | *小清爱提问：监督学习和无监督学习有什么不同？[] |
| | *小清爱提问：什么是强化学习？[] | | *小清爱提问：什么是强化学习？[] |
| − | *小清爱提问：？ []
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| | ==扩展阅读== | | ==扩展阅读== |
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| − | * 维基百科：没有免费的午餐定理 [http://aigraph.cslt.org/courses/12/No_free_lunch_theorem.pdf] | + | *DeepMind AlphaGo博客[https://www.deepmind.com/research/highlighted-research/alphago] |
| − | * 维基百科：梯度下降法[http://aigraph.cslt.org/courses/12/梯度下降法.pdf][http://aigraph.cslt.org/courses/12/Gradient_descent.pdf] | + | *维基百科: AlphaGo [http://aigraph.cslt.org/courses/13/AlphaGo.pdf][http://aigraph.cslt.org/courses/13/AlphaGo_chs.pdf] |
| − | * 百度百科：梯度下降法[https://baike.baidu.com/item/%E6%A2%AF%E5%BA%A6%E4%B8%8B%E9%99%8D/4864937][http://baike.baidu.com/l/FdY9mFXE] | + | *DeepMind AlphaStar博客[https://www.deepmind.com/blog/alphastar-mastering-the-real-time-strategy-game-starcraft-ii] |
| − | * 知乎：梯度下降法[https://zhuanlan.zhihu.com/p/36902908]
| + | *维基百科: AlphaStar[http://aigraph.cslt.org/courses/13/AlphaStar.pdf] |
| − | * 知乎：小批量梯度下降法[https://zhuanlan.zhihu.com/p/72929546]
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| − | * 知乎：动量梯度下降法[https://www.jiqizhixin.com/graph/technologies/d6ee5e5b-43ff-4c41-87ff-f34c234d0e32][]
| + | ==视频展示== |
| − | * 维基百科：模拟退火算法 [http://aigraph.cslt.org/courses/12/模拟退.pdf火][http://aigraph.cslt.org/courses/12/Simulated_annealing.pdf] | + | *小清爱提问：什么是聚类？ [https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247487378&idx=1&sn=bd2ec82d7baf0d4c3074f2b09bd678aa&chksm=c3080550f47f8c46308ce16dfe3facff9f9f09482c0da5ceb50c2ed0ba0043e5e3960bbf7df6&scene=178#rd] |
| − | * 百度百科：模拟退火算法[https://baike.baidu.com/item/%E6%A8%A1%E6%8B%9F%E9%80%80%E7%81%AB%E7%AE%97%E6%B3%95/355508][http://baike.baidu.com/l/Smyp3NfN] | + | *小清爱提问：什么是流形学习？[] |
| − | * 知乎：模拟退火详解 [https://zhuanlan.zhihu.com/p/266874840] | + | *小清爱提问：机器学习里有哪些回归模型[] |
| − | * 维基百科：牛顿法 [http://aigraph.cslt.org/courses/12/Newton's_method.pdf][http://aigraph.cslt.org/courses/12/牛顿法.pdf] | + | *小清爱提问：机器学习里有哪些分类模型[https://mp.weixin.qq.com/s?__biz=Mzk0NjIzMzI2MQ==&mid=2247486850&idx=1&sn=313502e7f4533d70fc627240df7fc4db&chksm=c3080740f47f8e56dbb88a8f9bdbf4486843b3a6a5b4a6dd31061cbcbca1f5f0f97cfbee87b6&scene=178#rd] |
| − | * 维基百科：奥卡姆剃刀[http://aigraph.cslt.org/courses/12/奥卡姆剃刀.pdf][http://aigraph.cslt.org/courses/12/Occam's_razor.pdf]
| + | *UC Berkley的科学家用一小时教会机器人站立、抓取等动作 [http://aigraph.cslt.org/courses/08/DayDreamer.mp4][https://arxiv.org/pdf/2206.14176.pdf][https://danijar.com/project/daydreamer/] |
| − | * 百度百科：奥卡姆剃刀[https://baike.baidu.com/item/%E5%A5%A5%E5%8D%A1%E5%A7%86%E5%89%83%E5%88%80%E5%8E%9F%E7%90%86/10900565][http://baike.baidu.com/l/HUkXrXzT] | + | |
| − | * 维基百科：过拟合[http://aigraph.cslt.org/courses/12/Overfitting.pdf][http://aigraph.cslt.org/courses/12/過適.pdf] | + | |
| − | * 维基百科：GPT-3 [http://aigraph.cslt.org/courses/12/GPT-3-zh.pdf][http://aigraph.cslt.org/courses/12GPT-3-zh.pdf/]
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| − | * 机器之心：当谈论机器学习中的公平公正时，我们该谈论些什么？[https://www.jiqizhixin.com/articles/2020-06-03-11]
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| − | * 机器之心：数据增强 [https://www.jiqizhixin.com/articles/2019-12-04-10]
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| − | * 知乎：数据增强 [https://zhuanlan.zhihu.com/p/38345420][https://zhuanlan.zhihu.com/p/41679153]
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| − | * 什么是模型预训练[https://paddlepedia.readthedocs.io/en/latest/tutorials/pretrain_model/pretrain_model_description.html]
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| − | * 迁移学习 [https://baike.baidu.com/item/%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0/22768151]
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| − | * 优化方法在线演示 [https://www.benfrederickson.com/numerical-optimization/] | + | * 回归任务演示 [https://www.benfrederickson.com/numerical-optimization/] |
| − | * 基于神经网络的二分类任务演示 [https://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html] | + | * 分类任务演示 [https://cs.stanford.edu/people/karpathy/convnetjs/demo/classify2d.html] |
| | + | * 聚类演示 [http://alekseynp.com/viz/k-means.html] |
| | + | * 流形学习演示 （tSNE) [https://cs.stanford.edu/people/karpathy/tsnejs/csvdemo.html][https://projector.tensorflow.org/] |
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| | ==开发者资源== | | ==开发者资源== |
| | + | |
| | + | * 用于机器学习的Sklearn Python工具包，包含回归，分类，聚类，流形学习等各种函数库 [https://scikit-learn.org/stable/] |
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| | ==高级读者== | | ==高级读者== |
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| − | * 王东，机器学习导论,第一章“绪论”，第十一章“优化方法”[http://mlbook.cslt.org] | + | * 王东，机器学习导论,2021,清华大学出版社 [http://mlbook.cslt.org] |
| − | * Wolpert, David (1996), "The Lack of A Priori Distinctions between Learning Algorithms", Neural Computation, pp. 1341–1390 [https://web.archive.org/web/20161220125415/http://www.zabaras.com/Courses/BayesianComputing/Papers/lack_of_a_priori_distinctions_wolpert.pdf] | + | * 周志华，机器学习,2016 [https://item.jd.com/11867803.html] |
| − | * Sebastian Ruder, An overview of gradient descend algorithms,2017 [https://arxiv.org/pdf/1609.04747.pdf]
| + | |
| − | * Kirkpatrick, S.; Gelatt Jr, C. D.; Vecchi, M. P. (1983). "Optimization by Simulated Annealing". Science. 220 (4598): 671–680. [https://sci2s.ugr.es/sites/default/files/files/Teaching/GraduatesCourses/Metaheuristicas/Bibliography/1983-Science-Kirkpatrick-sim_anneal.pdf]
| + | |
| − | * Brown et al., Language Models are Few-Shot Learners [https://arxiv.org/pdf/2005.14165.pdf]
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