From a3f5a2d7a0b8f6d0e4e60165d2307841445f55f5 Mon Sep 17 00:00:00 2001 From: Yi Wang Date: Fri, 15 Sep 2017 12:12:17 -0700 Subject: [PATCH] Change according to comments from Abhinav and Xi --- 02.recognize_digits/README.md | 6 +++--- 02.recognize_digits/index.html | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/02.recognize_digits/README.md b/02.recognize_digits/README.md index 271557a..3e0d602 100644 --- a/02.recognize_digits/README.md +++ b/02.recognize_digits/README.md @@ -1,9 +1,9 @@ # Recognize Digits -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). +The source code for this tutorial locates in [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). ## 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, 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. +When one learns to program, the first task is usually to write a program that prints "Hello World!". In Machine Learning or Deep Learning, an equivalent task is to train a model to recognize hand-written digits using the [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. 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.


@@ -12,7 +12,7 @@ Fig. 1. Examples of MNIST images The MNIST dataset is 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. 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 of 60,000 samples and test set of 10,000 samples. 250 annotators labeled the training set, thus guaranteed that there wasn't a complete overlap of annotators of training set and test set. -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). +The MNIST dataset has been used for evaluating many image recognition algorithms such as a 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). In this tutorial, we start with a simple **softmax** regression model and go on with MLP and CNN. Readers will see how these methods improve the recognition accuracy step-by-step. diff --git a/02.recognize_digits/index.html b/02.recognize_digits/index.html index 6b03f1e..0b0b4d5 100644 --- a/02.recognize_digits/index.html +++ b/02.recognize_digits/index.html @@ -42,10 +42,10 @@