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吴恩达 Machine Learning Yearning 完整版 中文版+英文原版 评分:
吴恩达新书 Machine Learning Yearning 的完整版(中译版 + 英文原版) 恩达写这本书的目的: AI,机器学习和深度学习正在逐渐改变越来越多的行业。我写这本书《Machine Learning Yearning》的目的就是教会大家如何构建自己的机器学习项目。 这本书的目的不是教你机器学习算法理论,而是教你如何使用这些算法。一些技术AI课程会给你锤子工具,而这本书就是让你学会如何使用这些锤子工具。如果你致力于成为AI技术领导者并渴望为你的团队找到正确的方向。 阅读《Machine Learning Yearning》之后,你将能够: 优先考虑AI项目最有前途的方向。 调试机器学习项目中的错误。 在复杂设置中构建ML,例如训练/测试样本不匹配。 构建一个ML项目,接近甚至达到人类水平。 知道什么时候、如何使用端对端学习、迁移学习和多任务学习。
上传时间:2018-10 大小:8.99MB
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【首发】吴恩达machine learning yearning英文 完整书签 文字版
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2018-05-04吴恩达 machine learning 公开课讲义 吴恩达 machine learning 公开课讲义 吴恩达 machine learning 公开课讲义 吴恩达 machine learning 公开课讲义
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(全)2020吴恩达机器学习MachineLearning课程编程作业
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2018-09-27和吴恩达其他课类型不同,这本书并不属于教材型读物,更偏向于实战经验技巧的汇总,共分成57个小节,每节从示例入手,推荐干货技巧。
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MachineLearning Yearning
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2018 吴恩达(Andrew Ng)新书《Machine Learning Yearning》50~52
2018-07-162018 吴恩达(Andrew Ng)新书《Machine Learning Yearning》50~52pdf
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吴恩达《Machine Learning Yearning》 中英电子版
2018-10-28吴恩达新书,《Machine Learning Yearning》 中英电子版
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Machine Learning (Tom) 中文清晰版
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machine learning yearning Andrew Ng
2018-09-30machine learning yearning是吴恩达新书,本书含有58章
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Machine_Learning_Yearning_V01
2018-04-26Table of Contents (draft) Why Machine Learning Strategy 4 ........................................................................................... How to use this book to help your team 6 ................................................................................ Prerequisites and Notation 7 .................................................................................................... Scale drives machine learning progress 8 ................................................................................ Your development and test sets 11 ............................................................................................ Your dev and test sets should come from the same distribution 13 ........................................ How large do the dev/test sets need to be? 15 .......................................................................... Establish a single-number evaluation metric for your team to optimize 16 ........................... Optimizing and satisficing metrics 18 ..................................................................................... Having a dev set and metric speeds up iterations 20 ............................................................... When to change dev/test sets and metrics 21 .......................................................................... Takeaways: Setting up development and test sets 23 .............................................................. Build your first system quickly, then iterate 25 ........................................................................ Error analysis: Look at dev set examples to evaluate ideas 26 ................................................ Evaluate multiple ideas in parallel during error analysis 28 ................................................... If you have a large dev set, split it into two subsets, only one of which you look at 30 ........... How big should the Eyeball and Blackbox dev sets be? 32 ...................................................... Takeaways: Basic error analysis 34 .......................................................................................... Bias and Variance: The two big sources of error 36 ................................................................. Examples of Bias and Variance 38 ............................................................................................ Comparing to the optimal error rate 39 ................................................................................... Addressing Bias and Variance 41 .............................................................................................. Bias vs. Variance tradeoff 42 ..................................................................................................... Techniques for reducing avoidable bias 43 .............................................................................. Techniques for reducing Variance 44 ....................................................................................... Error analysis on the training set 46 ........................................................................................ Diagnosing bias and variance: Learning curves 48 ................................................................. Plotting training error 50 .......................................................................................................... Interpreting learning curves: High bias 51 ............................................................................... Interpreting learning curves: Other cases 53 .......................................................................... Plotting learning curves 55 ....................................................................................................... Why we compare to human-level performance 58 .................................................................. How to define human-level performance 60 ........................................................................... Surpassing human-level performance 61 ................................................................................ Why train and test on different distributions 63 ...................................................................... Page!2 Machine Learning Yearning-Draft V0.5 Andrew NgWhether to use all your data 65 ................................................................................................ Whether to include inconsistent data 67 .................................................................................. Weighting data 68 .................................................................................................................... Generalizing from the training set to the dev set 69 ................................................................ Addressing Bias and Variance 71 ............................................................................................. Addressing data mismatch 72 ................................................................................................... Artificial data synthesis 73 ........................................................................................................ The Optimization Verification test 76 ...................................................................................... General form of Optimization Verification test 78 ................................................................... Reinforcement learning example 79 ......................................................................................... The rise of end-to-end learning 82 ........................................................................................... More end-to-end learning examples 84 .................................................................................. Pros and cons of end-to-end learning 86 ................................................................................ Learned sub-components 88 .................................................................................................... Directly learning rich outputs 89 .............................................................................................. Error Analysis by Parts 93 ....................................................................................................... Beyond supervised learning: What’s next? 94 ......................................................................... Building a superhero team - Get your teammates to read this 96 ........................................... Big picture 98 ............................................................................................................................ Credits 99
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吴恩达机器学习视频百度网盘(视频+PPT+个人笔记+作业)
2019-01-13本文档包括吴恩达机器学习视频百度网盘(视频+PPT+个人笔记+作业)