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1. Introduction and structural parameters of BP neural network Neural network is a common mathematical model in machine learning. It processes information by constructing a structure similar to the synaptic connection of brain nerves. In the process of applying neural network, the units dealing with information are generally divided into t ...

Article Directory Preface1. What is pandas?2. Steps for use 1. Introducing Libraries2. Read in datasummary Preface With the continuous development of in-depth learning, neural networks are becoming more and more popular. As we all know, cnn is very effective in image classification. This paper will show a simple convolution neural n ...

Download address Link: https://pan.baidu.com/s/1hqtBQf6jRJINx4AgB8S2Tw   Extraction code: zxkt            Chapter 8 of Volume II uses HDF5 and large data sets          So far, in this book, we have only used data sets suitable for the main memory of ...

Reference books 95 - neural network and deep learning - Qiu Xipeng 98 - in depth study of dynamics - the latest version in September 10, Introduction to artificial neural network, Xi. Training deep neural network, XII. Distributed TensorFlow     Perceptron, the input number is connected with the weight to form the sum, and then ...

Recurrent neural network (RNN) is designed based on the recursive nature of sequential data (such as language, speech and time series). It is a feedback type of neural network. Its structure includes loop and self repetition, so it is called "loop". It is specifically used to process sequence data, such as generating text word by word ...

When training the model with pytorch, optimizer.zero is usually used successively in the process of traversing epochs_ Grad(), loss. Backward, and optimizer.step(), LR_ The four functions of scheduler. Step() are as follows: train_loader=DataLoader( train_dataset, batch_size=2, shuffle=True ) model=myModel() criterion=nn.CrossEn ...

Method for obtaining picture mean and variance In domain adaptive change, or when processing data sets, it is often necessary to analyze the mean and variance of images or data sets By analyzing the mean and variance, we can efficiently obtain the data distribution, especially the large data set Therefore, here we record and introduce the ac ...

Task04 This study refers to Datawhale open source learning: https://github.com/datawhalechina/fantastic-matplotlib The content is generally derived from the original text and adjusted in combination with their own learning ideas. Personal summary: first, Matplotlib mainly has two ways to create text: pyplot API and objective oriented API. 2. Te ...

1 Introduction For the general classification, we import the data into the neural network, which tells us what kind it is. For some necessary cases, we need to know how the neural network makes judgment, and which parameters of the input data affect the judgment of the neural network. Therefore, the paper Learning Deep Features for Discriminat ...

Video link: The final collection of PyTorch deep learning practice_ Beep beep beep_ bilibili Idea: Prepare datasetDesign model classConstruct loss function and optimizerTraining and testing   1. Prepare dataset: Because MNIST is the data set of torchvision.datasets and a subclass of torch.utils.data.Dataset, you can directly use the data ...