diff --git a/recognize_digits/index.en.html b/recognize_digits/index.en.html index b26956e59e4a6292b19974f7b3b55cf7d18b91ce..b061957e2f396b892d2f8d33e3cd71a420e34a49 100644 --- a/recognize_digits/index.en.html +++ b/recognize_digits/index.en.html @@ -40,7 +40,7 @@ Source code of this tutorial is under [book/recognize_digits](https://github.com/PaddlePaddle/book/tree/develop/recognize_digits) For the first-time use, please refer to PaddlePaddle [installation instructions](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html). ## Introduction -When we learn programming, the first program is usually printing “Hello World.” In Machine Learning, or Deep Learning, this is handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains handwritten digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered. +When we learn programming, the first program is typically printing “Hello World.” In Machine Learning, or Deep Learning, this is usually handwritten digit recognition with [MNIST](http://yann.lecun.com/exdb/mnist/) dataset. Handwriting recognition is a typical image classification problem. The problem is relatively easy, and MNIST is a complete dataset. As a simple Computer Vision dataset, MNIST contains handwritten digits and corresponding labels (Fig. 1). An image is a 28x28 matrix, and a label corresponds to one of the 10 digits from 0 to 9. Each image is normalized in size and centered.