English Document Structure #620
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给个生成目录后的链接吧。这样很难review。 |
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@gangliao 这个PR里混合了文档结构调整（git mv）和新增的几个文档——包括很重要的Get Started文档。以后最好每个PR做一个特定的事儿。要不然可能每一批comments都很多，这样迭代起来就慢了。 我写了一些comments，但是没有时间看完全部新增文档。你可以按照我写的comments举一反三。 另外，建议英语文档写作的时候用 Grammerly 检查用一下语法。 |
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| Credits |
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下面的内容都和credit没有关系。credit是“归功于谁”的意思。下面两段话应该是 About 下面的内容，最后一段关于authors的应该是 Credit 下面的内容。
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| * [Model Config Interface](trainer_config_helpers/index.rst) | ||
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| ## Predict |
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Predict 这个词得改一下。上次我们讨论是说到，Paddle不只是用来训练supervised model的。Predict应该改成“Applications”。
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| Algorithm Configuration | |||
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Algorithm是指递归、动态规划、分治策略、搜索剪枝这些事儿。和RNN没半毛钱关系。这里是不是Chinglish了？实际上想说的是 Deep Models 吗？
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| ../../../tutorials/sentiment_analysis/bi_lstm.jpg | |||
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这个文件是一个symlibic link吗？为什么需要给图像文件建立symbolic link呢？如果是在rst文章里引用，那么在rst文件里引用 ../../../tuturials/sentiment_analysis/bi_lstm.jpg不行吗？
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这个symlibic link是之前就有，我也觉得可以去掉。
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| ../../../tutorials/text_generation/encoder-decoder-attention-model.png | |||
| Basic Usage | ||
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| PaddlePaddle is a deep learning platform open-sourced by Baidu. With PaddlePaddle, you can easily train a classic neural network within a couple lines of configuration, or you can build sophisticated models that provide state-of-the-art performance on difficult learning tasks like sentiment analysis, machine translation, image caption and so on. |
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这一段可以简化成：
Let's run Paddle to learn a very simple linear regression model!
| 1. A Classic Problem | ||
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| Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression <https://en.wikipedia.org/wiki/Simple_linear_regression>`_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets. |
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这一段写得和 https://en.wikipedia.org/wiki/Simple_linear_regression 里的描述重复了。我建议就用wikipedia里的原话，抄一遍就行
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@wangkuiyi 这篇其实不是我写的，我只是调整了文档所在的位置和格式风格。所以看起来是新增的。
要不先简单的改下，之后重新开一个PR, 单独改吧。
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| Now, to give you a hint of what using PaddlePaddle looks like, let's start with a fundamental learning problem - `simple linear regression <https://en.wikipedia.org/wiki/Simple_linear_regression>`_: you have observed a set of two-dimensional data points of ``X`` and ``Y``, where ``X`` is an explanatory variable and ``Y`` is corresponding dependent variable, and you want to recover the underlying correlation between ``X`` and ``Y``. Linear regression can be used in many practical scenarios. For example, ``X`` can be a variable about house size, and ``Y`` a variable about house price. You can build a model that captures relationship between them by observing real estate markets. | ||
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| 2. Prepare the Data |
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Prepare the Data 表意不明确—— 之前并没有提到什么data，何来 the data呢？
另外，这一节介绍的实际内容是 Load the Training Data，就以此为标题好了。
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| Suppose the true relationship can be characterized as ``Y = 2X + 0.3``, let's see how to recover this pattern only from observed data. Here is a piece of python code that feeds synthetic data to PaddlePaddle. The code is pretty self-explanatory, the only extra thing you need to add for PaddlePaddle is a definition of input data types. |
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这一节的重点是要介绍Paddle job通常是通过 Python 写的 data provider 来读取数据的。所以第一段的第一句应该是介绍 data provider的。比如：
A PaddlePaddle job usually loads the training data by implementing a Python data provider. A data provider is a Python function which is called by PaddlePaddel trainer program, so it could adapt to any data format. We can write data provider to read from local filesystem, HDFS, databases, S3 or almost anywhere. In this example, our data provider synthesize the training data by sampling from the line Y=2X + 0.3.
| x = random.random() | ||
| yield [x], [2*x+0.3] | ||
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| 3. Train a NeuralNetwork |
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NeuralNetwork ==> Neural Network
* remove v2 * remove mobile * fix_02
…ective_recompute fused linear and selective recompute
Issue #558