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.
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.
