Polish English writing in Chapter 2 #405

wangkuiyi · 2017-09-15T18:50:21Z

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putcn · 2017-09-15T18:53:24Z

02.recognize_digits/README.md

@@ -1,22 +1,20 @@
 # Recognize Digits

-The source code for this tutorial is live at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).
+The source code for this tutorial lives at [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits). For instructions on getting started with Paddle, please refer to [installation instructions](https://github.com/PaddlePaddle/book/blob/develop/README.md#running-the-book).


shall we change "lives" to "locates"?

abhinavarora · 2017-09-15T18:53:33Z

02.recognize_digits/README.md


 ## Introduction
-When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, the equivalent task is to train a model to recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.
+When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, a similar task is to train a model to recognize hand-written digits using the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.


Lets replace a similar task ... to an equivalent task ....

abhinavarora · 2017-09-15T18:55:08Z

02.recognize_digits/README.md


 ## Introduction
-When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, the equivalent task is to train a model to recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.


It should be on the MNIST dataset instead of on the dataset MNIST

abhinavarora · 2017-09-15T18:58:31Z

02.recognize_digits/README.md


-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.
+MNIST has been the tester of many image recognition technologies, including single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet\[[1](#references)\], K-Nearest Neighbors (k-NN) \[[2](#references)\], Support Vector Machine (SVM) \[[3](#references)\], Neural Networks \[[4-7](#references)\], Boosting \[[8](#references)\] and preprocessing methods like distortion removal, noise removal, and blurring.  Among these algorithms, the *Convolutional Neural Network* (CNN) has achieved a series of impressive results in Image Classification tasks, including VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial).


Lets begin the sentence as The MNIST dataset has been used for evaluating many image recognition algorithms such as a single layer classifier, Multilayer Perceptron ...

abhinavarora · 2017-09-15T19:00:56Z

Also run the pre-commit for converting markdown to html.

putcn

LGTM

Polish English writing in Chapter 2

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wangkuiyi requested review from abhinavarora, putcn, luotao1 and juliecbd September 15, 2017 18:50

putcn reviewed Sep 15, 2017

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abhinavarora reviewed Sep 15, 2017

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Yi Wang added 2 commits September 15, 2017 12:05

Update the HTML file

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Change according to comments from Abhinav and Xi

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putcn approved these changes Sep 15, 2017

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wangkuiyi merged commit ab16fb0 into PaddlePaddle:develop Sep 15, 2017

wangkuiyi deleted the polish-02 branch September 15, 2017 22:20

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Polish English writing in Chapter 2 #405

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		## Introduction
		When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, the equivalent task is to train a model to recognize hand-written digits on the dataset [MNIST](http://yann.lecun.com/exdb/mnist/). Handwriting recognition is a classic image classification problem. The problem is relatively easy and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains images of handwritten digits and their corresponding labels (Fig. 1). The input image is a $28\times28$ matrix, and the label is one of the digits from $0$ to $9$. All images are normalized, meaning that they are both rescaled and centered.


		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.
		MNIST has been the tester of many image recognition technologies, including single layer linear classifier, Multilayer Perceptron (MLP) and Multilayer CNN LeNet\[[1](#references)\], K-Nearest Neighbors (k-NN) \[[2](#references)\], Support Vector Machine (SVM) \[[3](#references)\], Neural Networks \[[4-7](#references)\], Boosting \[[8](#references)\] and preprocessing methods like distortion removal, noise removal, and blurring. Among these algorithms, the Convolutional Neural Network (CNN) has achieved a series of impressive results in Image Classification tasks, including VGGNet, GoogLeNet, and ResNet (See [Image Classification](https://github.com/PaddlePaddle/book/tree/develop/03.image_classification) tutorial).

Polish English writing in Chapter 2 #405

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