Why Machine Learning Strategy
三军未动，战略先行
机器学习是无数应用重要的应用的基础，包括：搜索引擎、垃圾邮件分类、语音识别和产品推荐等。如果你或者你的团队正在开发一个机器学习有关的应用，并且你想要借此提高自己的水平，那么这本书将会让你受益匪浅。
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.
Example: Building a cat picture startup 示例：创业——喵喵写真公司
让我们假定你将要创业，你计划创建一个能够给吸猫人提供琳琅满目的喵喵图片的公司。你可以用神经网络算法来建立一个计算机视觉系统，从而在图片中“找出”猫。
Say you’re building a startup that will provide an endless stream of cat pictures to cat lovers. You use a neural network to build a computer vision system for detecting cats in pictures.
不过你悲剧地发现你的学习算法的准确性太差，你现在压力山大，该如何提高喵喵的识别率的？
你的智囊有很多点子，例如：
- 添数据：收集喵喵的图片。
- 收集一个多样的训练集：例如，猫咪的位置很奇葩的图片；猫咪的毛色很诡异的图片；用不同的相机设备拍的图片等
- 精确地训练算法，在梯度下降算法上采用更小的梯度。
- 尝试更大的神经网络，添加更多的维度，隐藏单元或参数等。
- 尝试一个更小的神经网络。
- 添加更多的正则表达式（例如L2正则）
- 改变一下神经网络的结构（函数、隐藏单元的数量等等）
- …
如果你在这些方向上选了一条正确的道路，你将会建立一个主流的吸猫平台，并领导你的公司走向人生巅峰。如果你运气不好选错了，你将会浪费几个月的时间。你怎么办呢？
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?
Your team has a lot of ideas, such as:
- Get more data: Collect more pictures of cats.
- 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; ….
- Train the algorithm longer, by running more gradient descent iterations.
- Try a bigger neural network, with more layers/hidden units/parameters.
- Try a smaller neural network.
- Try adding regularization (such as L2 regularization).
- Change the neural network architecture (activation function, number of hidden units, etc.) • …
If you choose well among these possible directions, you’ll build the leading cat picture platform, and lead your company to success. If you choose poorly, you might waste months. How do you proceed?
这本书就是要告诉你遇到这种情况该如何选择。大多数的机器学习问题会留下许多线索，这些线索将告诉你什么方法行之有效，什么方法徒劳无功。关键就在与如何解读这些线索，进而节约你宝贵的科研时间。
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.
本人能力有限，如有错误欢迎改正，希望不吝赐教。
——译者：wexin_42141390 邮箱：[email protected]