That same filter representing a horizontal line can be applied to all three channels of the underlying image, R, G and B. Each image can be represented as a combination of red, green and blue light, which can be added in various ways to produce a certain image.

Think of a convolution as a way of mixing two functions by multiplying them. A filter superimposed on the first three rows will slide across them and then begin again with rows 4-6 of the same image. These ideas will be explored more thoroughly below. He received the bachelor‘s degree from Hebei University of Technology, P.R. China and the master's degree from Nankan University, P.R. Geometrically, if a scalar is a zero-dimensional point, then a vector is a one-dimensional line, a matrix is a two-dimensional plane, a stack of matrices is a three-dimensional cube, and when each element of those matrices has a stack of feature maps attached to it, you enter the fourth dimension. which attempts to showcase the application of CNN’s to the stock market. This is depicted in Figure 4. Convolutional networks are designed to reduce the dimensionality of images in a variety of ways. By learning different portions of a feature space, convolutional nets allow for easily scalable and robust feature engineering. [6] https://www.machinecurve.com/index.php/2020/01/15/how-to-use-batch-normalization-with-keras/. This will happen once again when the algorithm reaches the last 2 rows of the image. Then, we will predict the movement of the price in the next few minutes. His research interests are machine learning, Internet of things, big data and time series data analytics. Now, she works in School of Economics and Management, Zhengzhou University of Light Industry.

He has finished his Ph.D. program in School of Business of Beijing Normal University in China, and post-doctoral program in Institute of Economics of School of Social Sciences of Tsinghua University in China. I wouldn’t say this is anything novel.. but what I found interesting was that CNN’s aren’t only bound with computer vision when it comes to finance. If the two matrices have high values in the same positions, the dot product’s output will be high.


The depth is necessary because of how colors are encoded. China.Now, he worked in Planning and Development Office of China Development Bank Hebei Branch.

This helps in reducing overfitting.

Jun Liu was born in Zhengzhou, Henan province, P.R. A bi-weekly digest of AI use cases in the news.


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