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After reading this article, you can do the following two questions together: 104. Maximum depth of binary tree559.n maximum depth of fork tree Force button topic link (opens new window) Given a binary tree, find its maximum depth. The depth of the binary tree is the number of nodes on the longest path from the root node to the farthest ...

Impulse neural network is called the third generation neural network, which has higher biological reliability. SNN has always occupied the core position in the research of brain like science in recent years. When the performance is similar, the chip based on pulse neural network has lower power consumption, better stability and robustness than ...

This article is right [hard big data learning route] learning guide for experts from zero to big data (fully upgraded version) HBase partial supplement. 1 high availability In HBase, HMaster is responsible for monitoring the lifecycle of HRegionServer and balancing the load of regional server. If HMaster fails, the whole HBase cluster will fa ...

Abstract Over the past two years, OF has been paying attention to house prices. Putting aside some external factors such as policies and real estate speculation, what are the main factors affecting house prices for the house itself? OF selected several factors for analysis: house age, area, number OF floors (1 / 1.5 / 2 / 2.5 /...), number O ...

Introduction of BP network prediction algorithm 1. Introduction to Artificial Neural Network Artificial Neural Network (ANN) is a computer system formed by several very simple processing units connected to each other in some way to mimic the structure and function of the human brain. It processes information by the dynamic response of its sta ...

1. Introduction to Sparse Representation 1 Sparse Representation Theory Sparse means that most or all of the original signals are represented by a linear combination of fewer basic signals. After sparse representation, the more sparse the signal is, the more accurate the reconstructed signal will be. Set x< Rn as the signal to be processed ...

5. Differences between kfold, structured kfold, structured shufflesplit and groupkfold and implementation of structured group kfold In machine learning, we can't take the whole data set directly for training, but use the cross validation method for training. Enhance randomness and reduce noise to reduce over fitting, so as to obtain and le ...

Fundamentals of machine learning The essence of machine learning: using data to solve problems Data preprocessing (important in deep learning) - > training phase - > model generation - > prediction phase We usually choose some data as the test set, such as about 20%. Sometimes there is an additional verification set of about 20 ...

     When learning machine learning, many students start programming directly after a rough look at the theory, which is very commendable. However, it's not really a handwriting algorithm, but to directly call a package such as sklearn, which is not appropriate. The author is not saying that the package transfer is bad. In p ...

Pytorch machine learning (VIII) -- NMS non maximum suppression and DIOU-NMS improvement in yoov5 catalogue Pytorch machine learning (VIII) -- NMS non maximum suppression and DIOU-NMS improvement in yoov5 preface 1, NMS non maximum suppression algorithm 2, Hard NMS non maximum code 3, DIOU-NMS   4, Soft NMS preface ...