Summary of Convolutional Neural Network (CNN)

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

Learning Materials

  • 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.

to read

Extensions

Stacked with RNN

an effective approach might be to combine CNNs and RNNs in this way: first we use convolution and pooling layers to reduce the dimensionality of the input. This would give us a rather compressed representation of the original input with higher-level features. (from here)

一点想法

像Fourier analysis这种,用一组完备的基函数,去表示任意一个函数,这种研究,wavelet analysis, taylor expansion,这些感觉都是一个思路,只是不同的基函数。

那么,有没有研究用 非正交的、非完备的、冗余很大的 一组基函数,去展开任意一个函数的数学分支?

感觉这个和现在的各种机器学习的骚操作很像啊……