Fixing few typos in the English readme of Ch1 and Ch2 (#397)

* Fixing few typos in the English readme of Ch1 and Ch2
* Running pre-commit for converting markdown to html

01.fit_a_line/README.md

@@ -124,7 +124,7 @@ paddle.init(use_gpu=False, trainer_count=1)
 
 ### Model Configuration
 
-Logistic regression is essentially a fully-connected layer with linear activation:
+Linear regression is essentially a fully-connected layer with linear activation:
 
 ```python
 x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))

01.fit_a_line/index.html

@@ -166,7 +166,7 @@ paddle.init(use_gpu=False, trainer_count=1)
 
 ### Model Configuration
 
-Logistic regression is essentially a fully-connected layer with linear activation:
+Linear regression is essentially a fully-connected layer with linear activation:
 
 ```python
 x = paddle.layer.data(name='x', type=paddle.data_type.dense_vector(13))

02.recognize_digits/README.md

@@ -12,7 +12,7 @@ Fig. 1. Examples of MNIST images
 
 The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
 
-Yann LeCun, one of the founders of Deep Learning, have previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
+Yann LeCun, one of the founders of Deep Learning, has previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
 
 Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet. These algorithms quickly reduced test error from 12% to 0.7% \[[1](#references)\]. Since then, researchers have worked on many algorithms such as **K-Nearest Neighbors** (k-NN) \[[2](#references)\], **Support Vector Machine** (SVM) \[[3](#references)\], **Neural Networks** \[[4-7](#references)\] and **Boosting** \[[8](#references)\]. Various preprocessing methods like distortion removal, noise removal, and blurring, have also been applied to increase recognition accuracy.
 
@@ -221,7 +221,7 @@ trainer = paddle.trainer.SGD(cost=cost,
                              update_equation=optimizer)
 ```
 
-Then we specify the training data `paddle.dataset.movielens.train()` and testing data `paddle.dataset.movielens.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
+Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
 
 `shuffle` is a reader decorator. It takes in a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.
 

02.recognize_digits/index.html

@@ -54,7 +54,7 @@ Fig. 1. Examples of MNIST images
 
 The MNIST dataset is created from the [NIST](https://www.nist.gov/srd/nist-special-database-19) Special Database 3 (SD-3) and the Special Database 1 (SD-1). The SD-3 is labeled by the staff of the U.S. Census Bureau, while SD-1 is labeled by high school students the in U.S. Therefore the SD-3 is cleaner and easier to recognize than the SD-1 dataset. Yann LeCun et al. used half of the samples from each of SD-1 and SD-3 to create the MNIST training set (60,000 samples) and test set (10,000 samples), where training set was labeled by 250 different annotators, and it was guaranteed that there wasn't a complete overlap of annotators of training set and test set.
 
-Yann LeCun, one of the founders of Deep Learning, have previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
+Yann LeCun, one of the founders of Deep Learning, has previously made tremendous contributions to handwritten character recognition and proposed the **Convolutional Neural Network** (CNN), which drastically improved recognition capability for handwritten characters. CNNs are now a critical concept in Deep Learning. From the LeNet proposal by Yann LeCun, to those winning models in ImageNet competitions, such as VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial), CNNs have achieved a series of impressive results in Image Classification tasks.
 
 Many algorithms are tested on MNIST. In 1998, LeCun experimented with single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet. These algorithms quickly reduced test error from 12% to 0.7% \[[1](#references)\]. Since then, researchers have worked on many algorithms such as **K-Nearest Neighbors** (k-NN) \[[2](#references)\], **Support Vector Machine** (SVM) \[[3](#references)\], **Neural Networks** \[[4-7](#references)\] and **Boosting** \[[8](#references)\]. Various preprocessing methods like distortion removal, noise removal, and blurring, have also been applied to increase recognition accuracy.
 
@@ -263,7 +263,7 @@ trainer = paddle.trainer.SGD(cost=cost,
                              update_equation=optimizer)
 ```
 
-Then we specify the training data `paddle.dataset.movielens.train()` and testing data `paddle.dataset.movielens.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
+Then we specify the training data `paddle.dataset.mnist.train()` and testing data `paddle.dataset.mnist.test()`. These two methods are *reader creators*. Once called, a reader creator returns a *reader*.  A reader is a Python method, which, once called, returns a Python generator, which yields instances of data.
 
 `shuffle` is a reader decorator. It takes in a reader A as input and returns a new reader B. Under the hood, B calls A to read data in the following fashion: it copies in `buffer_size` instances at a time into a buffer, shuffles the data, and yields the shuffled instances one at a time. A large buffer size would yield very shuffled data.