实用机器学习

《MACHINE LEARNING YEARNING》

吴恩达新书手稿完工

machine-learning-yearning

http://www.mlyearning.org/ ： machine-learning-yearning 需要资源私信，免费共享，csdn默认最小资源分为1分，无法修改了，很抱歉！！！

maching learning yearing

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

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。

Stanford cs468 课件

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

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_V0.5_01.pdf

Andrew Ng的新书《Machine Learning Yearning》01部分

Machine Learning Yearning 中英文版

Machine Learning Yearning 中英文版

Machine_Learning_Yearning

Machine_Learning_Yearning

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 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_V03

Here are more chapters of Machine Learning Yearning. These, along with next week’s chapters, teach you how to use Bias and Variance to prioritize improvements to your ML project. Older ideas about Bias/Variance, for example the “Bias/Variance tradeoff”, are becoming less useful in the deep learning era, and modern ML needs updated guidelines. Read this week’s chapters to learn more!

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-（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

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将展示如何构建机器学习项目，使机器学习算法发挥作用。配合网易云吴恩达老师的视频一起食用效果更佳呦！

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.

Machine-Learning

机器学习 基于

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。

Machine Learning Yearning_V02

Here are more chapters of Machine Learning Yearning. When you start a new Machine Learning project, how do you pick the most promising directions to work on? These chapters describe the mechanics of the manual error analysis process, which will help you pick the most promising directions for your projects.

吴恩达：Machine+Learning+Yearning

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

Machine-Learning-I

机器学习I

ChatGPT教程（终极版）最全整理

这是一篇动了某些人利益的良心教程。 这是一篇姗姗来迟的ChatGPT教程。 纯小白关于ChatGPT入门，你看我这篇文章就够了。 如果你已经用上了ChatGPT，更要恭喜你挖到宝藏，后面的高级技巧一定能让你有收获。 文章包含以下内容： 一、ChatGPT是啥？有什么用； 二、ChatGPT如何注册； 三、ChatGPT使用方法； 四、用ChatGPT搞钱； 五、高级技巧；

博客中Kmeans以及FCM算法数据（免积分）

博客中Kmeans以及FCM算法的数据，包括IRIS鸢尾花数据集、Wine葡萄酒数据集、Seed小麦种子数据集、glass数据集、WDBD乳腺癌数据集，下载在直接存入项目文件夹即可，如果下载不了，可以私信我，看到后会及时回复。

基于Python+pytorch的图像处理+附完整代码图像处理，能够轻松实现图像的读取、显示、裁剪等还有机器学习等操作

Python+PyTorch：图像处理界的“瑞士军刀” 在图像处理这个充满魔法的世界里，Python和PyTorch这对黄金搭档，就像一位技艺高超的魔法师和一把无所不能的“瑞士军刀”，总能轻松解决各种看似棘手的难题。它们以高效、灵活和强大的特性，引领着图像处理技术的发展潮流，让无数开发者为之倾倒。Python，这位优雅的魔法师，以其简洁易懂的语法和丰富的库资源，赢得了广大开发者喜爱。无论是数据处理、机器学习还是深度学习，Python都能轻松应对，展现出其无与伦比的魅力。在图像处理领域，Python更是如鱼得水，通过OpenCV、PIL等库，能够轻松实现图像的读取、显示、裁剪、缩放、滤波等操作，让图像在指尖起舞。而PyTorch，这把图像处理界的“瑞士军刀”，则以其灵活性和易用性，成为深度学习领域的翘楚。它拥有强大的自动求导功能，能够轻松构建和训练复杂的神经网络模型。在图像处理中，PyTorch能够助力开发者构建出各种高效的图像识别、分割、生成等模型，让图像焕发出新的生机。想象一下，当你掌握了Python和PyTorch这对黄金搭档，就如同掌握了一把魔法杖和一把瑞士军刀。必然大可作为

hugging face的models-openai-clip-vit-large-patch14文件夹

用于无法访问hugging face并需要运行stable-diffusion-webui时使用

神经网络回归预测--气温数据集

神经网络回归预测--气温数据集