TF-IDF model for NLP text keyword extraction: epidemic text data analysis based on stuttering word segmentation and wordcloud

TF-IDF model: analysis of epidemic text data based on stuttering word segmentation and wordcloud Recently, we have made a text data analysis of China's policy on the COVID-19. Let's introduce the relevant knowledge to summarize and consolidate, and hope to help more people. 1, TF IDF: keyword extraction Stop words: stop words are words o ...

Posted by Dasndan on Fri, 13 May 2022 00:46:36 +0300

[deep learning experiment] the second time: analysis and prediction of influencing factors of fiscal revenue

Relevant knowledge Set variable X ( 0 ) = { X ( ...

Posted by Alecdude on Fri, 13 May 2022 00:36:23 +0300

TF2.0 basic method of text classification

This article is compiled from TF2 0 official tutorial( https://www.tensorflow.org/tutorials/keras/text_classification) The example of this article is to use IMDB's comment data for sentimental analysis: Data source address: https://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz 1. Load dataset Use TF keras. preprocessing. text_ datas ...

Posted by show8bbs on Thu, 12 May 2022 02:26:50 +0300

[PyTorch learning notes] 1.2 introduction to tensor

thumbnail: https://image.zhangxiann.com/...toc: truedate: 2020/2/5 20:39:20disqusId: zhangxiancategories: PyTorch tags: AI Deep Learning Code of this chapter: https://github.com/zhangxiann/PyTorch_Practice/blob/master/lesson1/tensor_introduce1.py https://github.com/zhangxiann/PyTorch_Practice/blob/master/lesson1/tensor_introduce1.py Tensor c ...

Posted by sean14592 on Wed, 11 May 2022 06:42:57 +0300

pytorch training model

1 preface This article belongs to a series of tutorials on semantic segmentation for pytoch in-depth learning. The contents of this series of articles are: Basic use of pytoch Explanation of semantic segmentation algorithm Since wechat does not allow external links, you need to click "read the original text" in the lower left ...

Posted by fireant on Sun, 08 May 2022 08:05:32 +0300

[from the official case study framework Keras] seq2seq based on character LSTM

[from the official case study framework Keras] seq2seq based on character LSTM Keras official case link Tensorflow official case link Paddle official case link Pytoch official case link Note: this series only helps you to quickly understand and learn, and can independently use the relevant framework for in-depth learning research. Please ...

Posted by sarah on Sun, 08 May 2022 07:29:41 +0300

Image recognition using ResNet18 residual network model of CIFAR10 data set - with an accuracy of 90%

Image recognition experiment using ResNet18 residual network model of CIFAR10 data set - accuracy of 90% (detailed notes attached) Auther:Yuandong Li Date: 2020/11/17 Welcome to my github: Li-Y-D The source code of this experiment and the code file in Jupyter Notebook format: Image-classification-CIFAR10-ResNet18 I Test requirements: Use Jup ...

Posted by cretam on Sat, 07 May 2022 04:50:48 +0300

Community project sharing | build a film recommendation system with Jina

We are in contact with the recommendation system every day, including short videos, e-commerce, takeout, performances, advertisementsToday, we will demonstrate the project of Jina AI Community user Li Achintya, who built a movie recommendation system with Jina.Overview of the principle of film recommendation systemIn this Demo, the author trans ...

Posted by shadowwebs on Fri, 06 May 2022 14:33:23 +0300

Common functions of Tensorflow2

Cast type Force tensor to this data type tf. Cast (tensor name, dtype = data type) Calculate the maximum and minimum values of elements in the tensor dimension tf. reduce_ Max (tensor name) tf. reduce_ Min (tensor name) import tensorflow as tf x1 = tf.constant([1, 2, 3], dtype=tf.float64) print(x1) x2 = tf.cast(x1, tf.int32) print(x2) pri ...

Posted by PHPycho on Fri, 06 May 2022 13:41:13 +0300

Implementation of neural network classification by PyTorch

1, Classification 1.1 data Create some fake data to simulate the real situation For example, the data of two quadratic distributions, but their mean values are different import torch import matplotlib.pyplot as plt # False data n_data = torch.ones(100, 2) # Basic form of data x0 = torch.normal(2*n_data, 1) # Type 0 x data (tens ...

Posted by M4F on Fri, 06 May 2022 11:39:22 +0300