Softmax regression, Multi-layer perceptron and Convolutional Neural Network in this chapter are the most basic Deep Learning models. More sophisticated models in the following chapters are derived from them. Therefore, these models are very helpful for the future learning. At the same time, we observed that when evolving from the simplest softmax regression to slightly complex Convolutional Neural Network, recognition accuracy on MNIST data set has large improvement, due to Convolutional layers' local connections and parameter sharing. When learning new models in the future, we hope readers to understand the key ideas for a new model to improve over an old one. Moreover, this chapter introduced basic flow of PaddlePaddle model design, starting from dataprovider, model layer construction, to final training and prediction. By becoming familiar with this flow, readers can use specific data and define specific network models, and complete training and prediction for their tasks.
## References
1. LeCun, Yann, Léon Bottou, Yoshua Bengio, and Patrick Haffner. ["Gradient-based learning applied to document recognition."](http://ieeexplore.ieee.org/abstract/document/726791/) Proceedings of the IEEE 86, no. 11 (1998): 2278-2324.
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