## Summary of Convolutional Neural Network (CNN)

I use CNN for time series prediction (1D), not for image works (2D or 3D).

# Learning Materials

- Introduction to 1D Convolutional Neural Networks in Keras for Time Sequences
- 各种细节都讲得很详细，尤其是 1D 和 2D 的对比，其实没有什么本质差别
- 下面这个图基本总结了使用
`Keras.layers.Conv1D`

的所有要点 - 此文有中文翻译。

- How to Develop 1D Convolutional Neural Network Models for Human Activity Recognition
- time series classification
- two 1D CNN layers, followed by a dropout layer for regularization, then a pooling layer. 为什么这样？
- It is common to define CNN layers in groups of two in order to give the model a good chance of learning features from the input data. 为什么这样？
- CNNs learn very quickly, so the dropout layer is intended to help slow down the learning process
- The pooling layer … consolidating them to only the most essential elements.

- After the CNN and pooling, the learned features are flattened to one long vector
- a standard configuration of 64 parallel feature maps and a kernel size of 3 (Where comes this “standard” configuration?)
- a multi-headed model, where each head of the model reads the input time steps using a different sized kernel.