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1, Brief introduction of BP neural network traffic sign recognition Road traffic signs are used to prohibit, warn, instruct and restrict road users to use roads in an orderly manner to ensure travel safety If the road traffic signs can be recognized automatically, the occurrence of road traffic accidents will be greatly reduced However, due to ...

Figure ImageNet of neural network? Open source millions of OGB benchmark data sets such as Stanford University In the small data full of "MNIST", does graph neural network also need a big benchmark such as "ImageNet"? Recently, Professor Jure Leskovec of Stanford University announced the open source of Open Graph Benchmar ...

1 sample processing 1.1 load sample code --- Titanic forecast Py (Part 1) import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from scipy import stats import pandas as pd import matplotlib.pyplot as plt import os os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE" def moving_average(a, w=10):#Define a function to calcu ...

Get more training data (data enhancement) Data enhancement using geometric transformations Geometric transformations such as flip, crop, rotation and translation are some commonly used data enhancement techniques. GAN based data enhancement Reduce network capacity The simplest way to prevent overfitting is to reduce the size of the mod ...

Creating a bitcoin trading robot will not lose money In this article, we will create a deep reinforcement learning agent to learn to make money through bitcoin trading. In this tutorial, we will use the PPO agent in OpenAIgym and the stable baselines Library (a branch of OpenAI's baselines Library). The purpose of this series of articles is to ...

This article mainly records the process of using machine learning to solve the problems I encounter. catalogue 1, Problem description 2, Data processing 2.1 treatment of missing values 2.2 abnormal value handling 2.3 characteristic Engineering 3, Dimensionality reduction 4, Model establishment 5, Model training 1, Problem description ...

1, Principle introduction RNN can remember context information, so it is often used to process time series data. In theory, RNN can memorize infinite length of historical information, but due to the accumulation of gradients, the amount of calculation is too large to be operated in practice. Therefore, in practice, RNN can only record the info ...

Paper core target Here, the author aims at the graph classification problem of GNN. Study the model level interpretation method. The specific way is to train a graph generator use f ( . ) f(.) ...

Today, I'm going to take a simple note of mobilenet. I'm afraid I'll forget the relevant contents of mobilenet. First of all, we need to know that mobilenet is a model framework that can be used on embedded devices. Therefore, its biggest advantage is that it has less parameters, and we still need to ensure that the accuracy of computer vision ...

The difference between the two methods: Sequential network building method. The upper output is the Sequential network structure of the lower input, but it is impossible to write some non Sequential network structures with hops. At this time, we can choose to use class to build a neural network structure, and class can be used to encapsulate a ...