Python数据挖掘与机器学习实战

Machine Learning Yearning_吴恩达_英文版+中文版

Machine Learning Yearning_英文版+中文版 (中文版会持续更新，并有更新的链接地址) 注：转载别人的，无商业目的，资源共享。

吴恩达-Machine Learning Yearning-（1-52）完结中英文版本

吴恩达-Machine Learning Yearning（1-52章）完结中英文版本

Machine Learning Yearning完整版

吴恩达的书，很全。合并而成，希望对大家有帮助。。。

Machine Learning Yearning 中英文版

Machine Learning Yearning 中英文版

Machine_Learning_Yearning_V0.5_01.pdf

Andrew Ng的新书《Machine Learning Yearning》01部分

吴恩达：Machine Learning Yearning

Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is ...

吴恩达新书-Machine learning Yearning

吴恩达新书《Machine learning Yearning》最新整理版，包括目前所有最新章节。

吴恩达-Machine Learning Yearning.pdf

吴恩达-Machine Learning Yearning.pdf

吴恩达机器学习深度学习笔记以及机器学习训练秘籍（吴恩达新书Machine Learning Yearning）

资源中包含三个PDF，分别是大牛整理的吴恩达机器学习视频课笔记完整版、深度学习笔记最新版以及吴恩达新书（Machine Learning Yearning）。三本书理论与实践结合，学习了机器学习算法后，Machine Learning Yearning...

2018 吴恩达（Andrew Ng）新书《Machine Learning Yearning》1~49全

2018 吴恩达（Andrew Ng）新书《Machine Learning Yearning》1~49全

吴恩达：Machine+Learning+Yearning

吴恩达新书《Machine_Learning_Yearning》。。。。。。

MachineLearning Yearning

Deep Learning.ai 公司的吴恩达博士进行撰写内容原创为吴恩达博士，学习小组成员只对文献内容进行翻译，对于翻译有误的部分，欢迎大家提出。欢迎大家一起努力学习、提高，共同进步！

machine learning yearning Ch. 1-14

吴恩达分享的链接国内下载不了，分享一下方便下载machine learning yearning Ch. 1-14

Machine Learning Yearning 0.5 英文原版

Machine Learning Yearning 0.5 英文原版 高清PDF版

Machine Learning Yearning（吴恩达老师最新章节）

Once you have identified whether your algorithm has high bias or variance, these chapters discuss specific techniques to address the two.

吴恩达《Machine Learning Yearning》

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。

2018 吴恩达（Andrew Ng）新书《Machine Learning Yearning》50~52

2018 吴恩达（Andrew Ng）新书《Machine Learning Yearning》50~52pdf

machine learning yearning Andrew Ng

machine learning yearning是吴恩达新书，本书含有58章

吴恩达（Andrew NG）新书《Machine Learning Yearning》

吴恩达（Andrew NG）新书稿《Machine Learning Yearning》完整版，共58章。

Machine Learning Yearning(吴恩达的书)--Andre Ng

Machine Learning Yearning-Draft Andrew Ng26 Error analysis on the training set 27 Techniques for reducing variance 28 Diagnosing bias and variance: Learning curves 29 Plotting training error 30 ...

Machine_Learning_Yearning_V01

Table 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

吴恩达 machine learning

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

Machine Learning Yearning (Andrew Ng)

吴恩达（Andrew Ng）在Coursa上的教程的概括性总结和实战经验。涵盖了吴老师纵横深度学习10多年的经验，是掌握深度学习不可多得的入门法宝。无论是网络选择，初始条件选择，参数调节，还是预处理，吴老师都给出了经验性指导。祝大家从此都能年薪破百万！

吴恩达《Machine Learning Yearning》 中英电子版

吴恩达新书，《Machine Learning Yearning》 中英电子版

Stanford cs468 课件

斯坦福 stanford CS468 : Machine Learning for 3D Data课件，第一部分。

Machine Learning Yearning

Machine learning is the foundation of countless important applications, including web search, email anti-spam, speech recognition, product recommendations, and more. I assume that you or your team is working on a machine learning application, and that you want to make rapid progress. This book will help you do so.

《MACHINE LEARNING YEARNING》

吴恩达新书手稿完工

machine-learning-yearning

《machine-learning-yearning》吴恩达老师 最终手稿，拿走不谢

maching learning yearing

maching learning yearing andrew ng 2018最新力作 Machine Learning Yearning is a deeplearning.ai project