processing

Import libraries from nltk.corpus import stopwords from textblob import TextBlob from textblob import Word Lower casing and removing punctuations df[‘Text’] = df[‘Text’].apply(lambda x: " “.join(x.lower() for x in x.split())) df[‘Text’] = df[‘Text’].str.replace(’[^\w\s]’,”) df.Text.head(5) Removal of stop words stop = stopwords.words(‘english’) df[‘Text’] = df[‘Text’].apply(lambda x: " ".join(x for x in x.split() if x not in stop)) Spelling correction df[‘Text’] = df[‘Text’].apply(lambda x: str(TextBlob(x). correct())) Lemmatization df[‘Text’] = df[‘Text’].apply(lambda x: " ".join([Word(word).

Note: I will NOT update this general list any more in the future. Instead, I'm maintaining a list of computer security conference ranking and statistic . As we all know, conference is more important than journal in computer science area. This is the reason why you see this ranking. This ranking is collected from Internet, only for reference, maybe not accurate, not complete and no copyright. (The original NUS 1999 report on CS conference rankings ) Some conferences accept multiple categories of papers. The rankings below are for the most prestigious category of paper at a given conference. All

自然语言处理、信息检索研究领域属于AI，rank1和rank2的会议都是不错的，值得研究~~~ 要不自己总搞不清自己的领域哪些会议好，哪些论文价值高，感谢bluejade师兄，虽然他可能看不到我在这里写，哈哈 网址在： http://www.ntu.edu.sg/home/assourav/crank.htm ，与我相关的是AI领域，已标红 copy如下： Computer Science Conference Rankings DISCLAIMER: The ranking of conferences are taken mostly from an informal external source. The detailed procedure behind the ranking is unknown to the author. These rankings do not necessarily represent my personal view either. There is a possibility that some of the rankings may not be accurate, may not reflect current status of the conferences accurately, may not be complete, and

Topic I: Artificial Intelligence / Machine Learning / Pattern Recognition Topic II: Databases / Knowledge and Data Management / Data Security / Web / Mining （由于长度关系，会议影响因子对比部分只帖了这两个热门Topic的Conf） Topic I: AAAI: American Association for AI National Conference (0.99) NIPS: Neural Information Processing Systems (0.98) IJCAI: Intl Joint Conf on AI (0.96) ICCV: Intl Conf on Computer Vision (0.96) CVPR: IEEE Conf on Comp Vision and Pattern Recognition (0.96) ICML: Intl Conf on Machine Learning (0.95) CSSAC: Cognitive Science Society Annual Conference (0.92) UAI: Conference on Uncertainty in AI (0.91)

使用如下的办法解决： sudo apt-get clean sudo apt-get update sudo apt-get install --reinstall python-minimal python-lockfile 来源： CSDN 作者： Androidfoolish 链接： https://blog.csdn.net/u010117864/article/details/104152746

https://blog.csdn.net/xiewenrui1996/article/details/103913963 《Python深度学习》 下面是你应该从这个例子中学到的要点。 ‰ 回归问题使用的损失函数与分类问题不同。回归常用的损失函数是均方误差（MSE）。 ‰ 同样，回归问题使用的评估指标也与分类问题不同。显而易见，精度的概念不适用于回 归问题。常见的回归指标是平均绝对误差（MAE）。 ‰ 如果输入数据的特征具有不同的取值范围，应该先进行预处理，对每个特征单独进行 缩放。 ‰ 如果可用的数据很少，使用 K 折验证可以可靠地评估模型。 ‰ 如果可用的训练数据很少，最好使用隐藏层较少（通常只有一到两个）的小型网络，以 避免严重的过拟合。 import keras keras . __version__ Let's take a look at the data : # 波士顿房价数据集 # 本节将要预测 20 世纪 70 年代中期波士顿郊区房屋价格的中位数，已知当时郊区的一些数 # 据点，比如犯罪率、当地房产税率等。本节用到的数据集与前面两个例子有一个有趣的区别。 # 它包含的数据点相对较少，只有 506 个，分为 404 个训练样本和 102 个测试样本。输入数据的 # 每个特征（比如犯罪率）都有不同的取值范围。例如，有些特性是比例，取值范围为 0~1；有 #