吴恩达（1-4）为何选择机器学习策略

翻译要求

• 吴恩达(1-4)Why Machine Learning Strategy

  吴恩达（1-4）为何选择机器学习策略

• 1 Why Machine Learning Strategy

  1为何选择机器学习策略

• 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.

  机器学习是无数重要应用程序的基础，包括网络搜索，电子邮件反垃圾邮件，语音识别，产品推荐等。假设您或您的团队正在开发机器学习应用程序，并且希望快速取得进展。这本书将会帮到您。

• Example: Building a cat picture startup

  示例：构建一个猫咪图片项目

• Say you’re building a startup that will provide an endless stream of cat pictures to cat lovers.

  假设您正在建立一个初创公司，为猫爱好者提供源源不断的猫图片。

• You use a neural network to build a computer vision system for detecting cats in pictures.

  您使用神经网络构建计算机视觉系统，用于检测图片中的猫。

• But tragically, your learning algorithm’s accuracy is not yet good enough. You are under tremendous pressure to improve your cat detector. What do you do?

  但悲催的是，你的学习算法的准确性还不够好。您正面临着改善猫咪探测器的巨大压力。你将怎样做？

• Your team has a lot of ideas, such as:

  您的团队有很多想法，例如：

• • Get more data: Collect more pictures of cats.

  •获取更多数据：收集更多猫的照片。

• • Collect a more diverse training set. For example, pictures of cats in unusual positions; cats with unusual coloration; pictures shot with a variety of camera settings; ….

  收集更多样化的训练集。例如，特殊姿势的或是颜色罕见的猫的图片;用各种相机参数拍摄的照片; ...。

• • Train the algorithm longer, by running more gradient descent iterations.

  •通过运行更多梯度下降迭代来更长时间地训练算法。

• • Try a bigger neural network, with more layers/hidden units/parameters.

  •尝试更大的神经网络，具有更多层/隐藏单元/参数。

• • Try a smaller neural network.

  •尝试更小的神经网络。

• • Try adding regularization (such as L2 regularization).

  •尝试添加正则化（例如L2正则化）。

• • Change the neural network architecture (activation function, number of hidden units, etc.)

  •更改神经网络架构（激活功能，隐藏单元数等）

• • …

  •......

• If you choose well among these possible directions, you’ll build the leading cat pictureplatform, and lead your company to success. If you choose poorly, you might waste months.How do you proceed?

  如果您在这些可能的方向中做出了很好的选择，那么您将构建领先的猫咪图片平台，并引领您的公司走向成功。如果你选择不好，你可能会浪费几个月。你该怎么办？

• This book will tell you how. Most machine learning problems leave clues that tell you what’s useful to try, and what’s not useful to try. Learning to read those clues will save you months or years of development time.

  这本书将告诉你如何。大多数机器学习问题都会留下线索，告诉你什么是有用的尝试，什么是没用的尝试。学习阅读这些线索将为您节省数月或数年的开发时间。

• 2 How to use this book to help your team

• After finishing this book, you will have a deep understanding of how to set technical direction for a machine learning project.

  读完本书后，您将深入了解如何为机器学习项目设置技术方向。

• But your teammates might not understand why you’re recommending a particular direction.Perhaps you want your team to define a single-number evaluation metric, but they aren’t convinced. How do you persuade them?

• That’s why I made the chapters short: So that you can print them out and get yourteammates to read just the 1-2 pages you need them to know.

  这就是为什么我让这些章节简短：这样你就可以将它们打印出来并让你的团队成员只阅读你需要让他们了解的那1-2页即可。

• A few changes in prioritization can have a huge effect on your team’s productivity. By helpingyour team with a few such changes, I hope that you can become the superhero of your team!

• 3 Prerequisites and Notation

• If you have taken a Machine Learning course such as my machine learning MOOC on Coursera, or if you have experience applying supervised learning, you will be able to understand this text.

  如果您参加过机器学习课程，例如我在Coursera的机器学习MOOC，或者您有应用监督学习的经验，您将能够理解本文。

• I assume you are familiar with supervised learning​: learning a function that maps from x to y, using labeled training examples (x,y). Supervised learning algorithms include linear regression, logistic regression, and neural networks. There are many forms of machine learning, but the majority of Machine Learning’s practical value today comes from supervised learning.

• I will frequently refer to neural networks (also known as “deep learning”). You’ll only need a basic understanding of what they are to follow this text.

  我会经常提到神经网络（也称为“深度学习”）。您只需要基本了解它们就可以跟上本文所讲内容。

• If you are not familiar with the concepts mentioned here, watch the first three weeks of videos in the Machine Learning course on Coursera at http://ml-class.org

• 4 Scale drives machine learning progress

• Many of the ideas of deep learning (neural networks) have been around for decades. Why are these ideas taking off now?

• Two of the biggest drivers of recent progress have been:

• • Data availability.​ People are now spending more time on digital devices (laptops, mobile devices). Their digital activities generate huge amounts of data that we can feed to our learning algorithms.

• • Computational scale. ​We started just a few years ago to be able to train neural networks that are big enough to take advantage of the huge datasets we now have.

• In detail, even as you accumulate more data, usually the performance of older learning algorithms, such as logistic regression, “plateaus.” This means its learning curve “flattens out,” and the algorithm stops improving even as you give it more data:

• It was as if the older algorithms didn’t know what to do with all the data we now have.

• If you train a small neutral network (NN) on the same supervised learning task, you might get slightly better performance:

• Here, by “Small NN” we mean a neural network with only a small number of hidden units/layers/parameters. Finally, if you train larger and larger neural networks, you can obtain even better performance: [1]

• Thus, you obtain the best performance when you (i) Train a very large neural network, so that you are on the green curve above; (ii) Have a huge amount of data.

• Many other details such as neural network architecture are also important, and there has been much innovation here. But one of the more reliable ways to improve an algorithm’s performance today is still to (i) train a bigger network and (ii) get more data.

• The process of how to accomplish (i) and (ii) are surprisingly complex. This book will discuss the details at length. We will start with general strategies that are useful for both traditional learning algorithms and neural networks, and build up to the most modern strategies for building deep learning systems.

• [1] This diagram shows NNs doing better in the regime of small datasets. This effect is less consistent than the effect of NNs doing well in the regime of huge datasets. In the small data regime, depending on how the features are hand-engineered, traditional algorithms may or may not do better. For example, if you have 20 training examples, it might not matter much whether you use logistic regression or a neural network; the hand-engineering of features will have a bigger effect than the choice of algorithm. But if you have 1 million examples, I would favor the neural network.

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