├── .gitignore ├── BeautifulSoup └── 获取指定url中的li标签内容.ipynb ├── README.md ├── argparse ├── .vscode │ └── settings.json ├── Untitled.ipynb └── example.py ├── bert ├── .png.png ├── .vscode │ └── settings.json ├── BERT的全部输出是13层.ipynb ├── SEP对bert模型的影响.ipynb ├── Sequence_Classification.ipynb ├── Untitled.ipynb ├── attention.png ├── bert_github官网例子.ipynb ├── bert指定字典.ipynb ├── bert的使用_博客教程.ipynb ├── dough.png ├── lick.png ├── show.py ├── showAttetntion.py ├── toast.png ├── tokenizer的使用_processing data.ipynb ├── tokenizer详解.ipynb ├── tokenize方法.ipynb ├── traingAndFine-tuning.ipynb ├── transformer中常用的术语解释及案例.ipynb ├── 使用bert得到attention.ipynb ├── 使用bert生成embedding.ipynb ├── 使用bert生成embedding的两种方法.ipynb ├── 使用tokenizer对List[str]进行处理.ipynb ├── 加载bert模型时的配置输出.ipynb ├── 得到tokenize后的单词偏移位置.ipynb ├── 得到某条句子中的embedding.ipynb ├── 查看bert模型中的参数量.ipynb ├── 由id获取文本.ipynb └── 预测MASK的值.ipynb ├── cleanlab ├── .ipynb_checkpoints │ └── 第一行代码-checkpoint.ipynb ├── Untitled.ipynb ├── 使用cleanlab找出MNIST数据集中的错误.ipynb └── 第一行代码.ipynb ├── dgl ├── EdgeWeightNorm的使用.ipynb ├── GraphConv中使用加权边.ipynb ├── HeteroGraphConv中使用加权边.ipynb ├── HeteroGraph的使用.ipynb ├── RGCN的定义与使用.ipynb ├── Untitled.ipynb ├── dgl图的可视化.ipynb ├── updata_all的使用.ipynb ├── 入门教程一之用DGL建图.ipynb ├── 入门教程之二节点分类.ipynb ├── 入门教程之异构图建图.ipynb ├── 图batch操作.ipynb ├── 在异构图上进行消息传递.ipynb ├── 异质图带边权.ipynb ├── 查看异质图的边、节点数.ipynb ├── 查看异质图的边信息.ipynb ├── 查看异质图的边类型个数.ipynb ├── 根据tensor创建异质图.ipynb └── 给异质图赋边权.ipynb ├── fastNLP ├── DataSet的使用.ipynb ├── StaticEmbedding的使用.ipynb ├── Untitled.ipynb └── Vocabulary的使用.ipynb ├── git ├── hello └── readme.txt ├── keras └── Untitled.ipynb ├── matplotlib ├── EICR模型和NEPG模型的对比.ipynb ├── attention.png ├── defi_num.png ├── ijcnn_超参运行性能图.ipynb ├── matplotlib画图2.ipynb ├── shuffle的使用.ipynb ├── tex_demo.png ├── word2vec的向量展示.ipynb ├── 使用attention_score画图.ipynb ├── 使用matplotlib画一个矩阵热力图.ipynb ├── 同一个窗口画多个图形.ipynb ├── 在图形中使用latex公式.ipynb ├── 定义数图像.ipynb ├── 实体长度性能图.ipynb ├── 展示一个二维图形.ipynb ├── 根据二维矩阵画图.ipynb ├── 模式识别的作业.ipynb ├── 画对数函数+熵值图像.ipynb ├── 直方图.ipynb ├── 绘图教程一.ipynb ├── 绘图教程二之柱状图.ipynb └── 饼图.ipynb ├── numpy ├── arange()方法的使用.ipynb ├── array的切片操作.ipynb ├── array的用法.ipynb ├── cumsum方法的使用.ipynb ├── delete()的使用.ipynb ├── np.argmax()方法.ipynb ├── npz文件的读取.ipynb ├── numpy创建矩阵的几种方式.ipynb ├── prod函数的使用.ipynb ├── random.choice()方法的使用.ipynb ├── tril&triu函数.ipynb ├── unique()方法的使用.ipynb ├── 从array中选择符号的下标对另一个array进行修改.ipynb ├── 使用numpy完成线性变换.ipynb ├── 创建数组&asarray的使用.ipynb ├── 创建矩阵的方法总结.ipynb ├── 找出大于某个值的下标.ipynb ├── 生成固定步长的数组.ipynb ├── 获取最值及其下标.ipynb └── 输出矩阵的维度.ipynb ├── pandas └── 使用pandas读取csv文件.ipynb ├── pdfminer ├── 从pdf提取信息.ipynb ├── 勉刘申.pdf └── 泸州老窖2019年报.pdf ├── pickle ├── pickle.txt └── pickle入门使用.ipynb ├── python ├── 5种函数参数.ipynb ├── Counter的使用.ipynb ├── barChart.jpg ├── classmethod的使用.ipynb ├── copy函数的使用.ipynb ├── count函数的使用.ipynb ├── defi_emb.txt ├── del的使用.ipynb ├── dict的研究.ipynb ├── dir函数的使用.ipynb ├── divmod函数直接获取除数和商.ipynb ├── enum.ipynb ├── enumerate函数的使用.ipynb ├── extend与append方法的使用.ipynb ├── fire库的使用.ipynb ├── for循环之列表推导.ipynb ├── f表达式的使用.ipynb ├── help函数.ipynb ├── id.json ├── index()函数.ipynb ├── isalpha函数.ipynb ├── isdigit函数.ipynb ├── iterator和iterable.ipynb ├── join的使用.ipynb ├── json文件操作.ipynb ├── lambda.ipynb ├── list中的常用函数.ipynb ├── list删除操作.ipynb ├── list生成一维数组.ipynb ├── list的reverse函数.ipynb ├── list的切片操作.ipynb ├── list的坑.ipynb ├── list的复制操作.ipynb ├── list详解.ipynb ├── list转dict.ipynb ├── list遍历取到最后一位.ipynb ├── logging模块的使用.ipynb ├── logging的使用.ipynb ├── map操作.ipynb ├── numpy中的array.ipynb ├── open.txt ├── opensdf.txt ├── open函数的使用.ipynb ├── os.path.dirname方法获取指定文件的目录.ipynb ├── pow函数计算幂值.ipynb ├── punctuation字符.ipynb ├── python常用内置函数.ipynb ├── python常用函数.ipynb ├── python注释机制.ipynb ├── random函数.ipynb ├── range函数的使用.ipynb ├── read.txt ├── read和readline()函数.ipynb ├── re库的使用.ipynb ├── set(集合).ipynb ├── setdefault方法的使用.ipynb ├── shuffle两个不同的数组但保持对应不变.ipynb ├── starswith函数的使用.ipynb ├── staticmethod.ipynb ├── strip函数.ipynb ├── temp.ipynb ├── test.csv ├── time包_获取当前时间.ipynb ├── tqdm的使用.ipynb ├── try_catch模块.ipynb ├── unsquenze.ipynb ├── with的使用.ipynb ├── xlsx文件操作.ipynb ├── yield.ipynb ├── zip函数的使用.ipynb ├── 二重循环的简便写法.ipynb ├── 优先队列.ipynb ├── 使用Python完成ACM程序读入操作.ipynb ├── 使用Python的生成表达式生成一个杨辉三角.ipynb ├── 使用global操作全局变量.ipynb ├── 使用python生成随机数.ipynb ├── 使用random.choice从list中随机选择一个数.ipynb ├── 使用random随机找出某个数.ipynb ├── 元类.ipynb ├── 函数.ipynb ├── 列表复制的问题.ipynb ├── 创建文件夹.ipynb ├── 判断一个字符串是否是数字.ipynb ├── 判断并创建目录.ipynb ├── 单例模式.ipynb ├── 子串查找操作.ipynb ├── 字典.ipynb ├── 字典的合并.ipynb ├── 字符串.ipynb ├── 字符串分割方法.ipynb ├── 字符串常用函数.ipynb ├── 字符串转义.ipynb ├── 实例属性与类属性.ipynb ├── 实例方法+普通方法.ipynb ├── 实现tf-idf算法.ipynb ├── 对字典内容进行shuffle.ipynb ├── 对字典进行排序.ipynb ├── 将list中的每个小list扩展成相同大小.ipynb ├── 将list的值写入文件.ipynb ├── 将str强转成dict.ipynb ├── 将字典形式的字符串数据变成字典.ipynb ├── 嵌套list的flat.ipynb ├── 嵌套函数.ipynb ├── 常用包.ipynb ├── 常用数学函数.ipynb ├── 序列分类.ipynb ├── 找出list中最大值的下标.ipynb ├── 数值处理.ipynb ├── 数据处理常用函数_自实现.ipynb ├── 文件操作.ipynb ├── 文本处理.ipynb ├── 格式化字符串f_string.ipynb ├── 比较list是否相同.ipynb ├── 生成二维数组.ipynb ├── 画一个柱状图.ipynb ├── 直接用max_min取list中的最值.ipynb ├── 科学计数法.ipynb ├── 类变量+实例变量+方法变量.ipynb ├── 类继承.ipynb ├── 自定义异常代码.ipynb ├── 读取txt文件.ipynb ├── 读取数据文本长度，并以直方图显示.ipynb ├── 输出代码的运行时间.ipynb ├── 输出指定位的小数.ipynb └── 队列的使用.ipynb ├── pytorch ├── BCELoss的学习.ipynb ├── BCEWithLogitsLoss.ipynb ├── Batchnorm.ipynb ├── ConstantPad1d函数线性填充数据.ipynb ├── CrossEntropyLoss的使用.ipynb ├── GPU与CPU下的计算精度问题.ipynb ├── LayerNorm解析.ipynb ├── Linear类的使用.ipynb ├── PairwiseDistance的使用.ipynb ├── README.md ├── Untitled.ipynb ├── Untitled1.ipynb ├── Untitled2.ipynb ├── What-is-pytorch.ipynb ├── arange函数.ipynb ├── argmax函数的使用.ipynb ├── argsort函数的使用.ipynb ├── cat函数的使用.ipynb ├── cifar10_tutorial.ipynb ├── copy_函数的使用.ipynb ├── deep_learning_tutorial.ipynb ├── empty函数的使用.ipynb ├── eq函数的使用.ipynb ├── expand的使用.ipynb ├── fill函数的使用.ipynb ├── full()函数.ipynb ├── gather方法.ipynb ├── ge函数.ipynb ├── index_select函数的使用.ipynb ├── list转为tensor+stack函数的使用.ipynb ├── log_softmax的实现.ipynb ├── logsumexp函数的使用.ipynb ├── mask_fill方法的使用.ipynb ├── masked_select的使用.ipynb ├── matmul方法的使用.ipynb ├── max函数的使用.ipynb ├── neural_networks_tutorial.ipynb ├── nltk初探.ipynb ├── nn.Embedding的使用.ipynb ├── normalize的使用.ipynb ├── ones函数.ipynb ├── picture.png ├── plot画图技巧.ipynb ├── pytorch入门实战1.ipynb ├── randint的使用.ipynb ├── randn函数的使用.ipynb ├── random_函数的使用.ipynb ├── repeat函数的用法.ipynb ├── round函数.ipynb ├── sigmoid函数的使用.ipynb ├── softmax函数引发的血灾.ipynb ├── softmax函数的使用.ipynb ├── split函数.ipynb ├── squeeze()函数的使用.ipynb ├── stack函数的使用.ipynb ├── temp.ipynb ├── tensor和常数的比较.ipynb ├── tensor的切片操作.ipynb ├── tensor的维度输出的两种方法.ipynb ├── tensor维度问题.ipynb ├── tensor详解.ipynb ├── tensor转numpy.ipynb ├── test.json ├── topk函数的使用.ipynb ├── torch.Tensor与torch.tensor的区别.ipynb ├── torch中的矩阵操作.ipynb ├── torch常用函数.ipynb ├── transpose函数的使用.ipynb ├── unsqueeze函数.ipynb ├── untitled.txt ├── view函数和squeeze函数的区别.ipynb ├── view函数解析.ipynb ├── vocab.txt ├── zeros方法的使用.ipynb ├── 从二维tensor中删除值相同的对.ipynb ├── 使用numpy.ipynb ├── 使用pytorch加载数据.ipynb ├── 使用pytorch定义一个神经网络.ipynb ├── 加法总结.ipynb ├── 单位阵相关操作.ipynb ├── 反向传播.ipynb ├── 各种数据类型之间的相互转换.ipynb ├── 向量乘法.ipynb ├── 向量和矩阵相乘.ipynb ├── 向量放缩运算.ipynb ├── 向量（tensor）的异或操作.ipynb ├── 多标签计算损失.ipynb ├── 字典值对应生成矩阵.ipynb ├── 对tensor进行pad操作.ipynb ├── 将list中的tensor合并成一个tensor.ipynb ├── 打印模型参数.ipynb ├── 找出数组中大于指定值的下标.ipynb ├── 数据类型的强制转换.ipynb ├── 新建tensor的几种方法.ipynb ├── 构造一个新tensor.ipynb ├── 比较embedding的相似度.ipynb ├── 矩阵乘法总结.ipynb ├── 获取tensor中指定下标的内容.ipynb ├── 获取各矩阵对应位置的最大值.ipynb ├── 获取指定值的下标.ipynb ├── 计算一个矩阵行向量之间的相似度.ipynb ├── 计算相似度.ipynb ├── 逻辑回归.ipynb ├── 遍历多维tensor.ipynb └── 高维数组加低维数组.ipynb ├── seqeval └── classification_report的使用.ipynb ├── sklearn ├── StratifiedKFold.ipynb ├── Untitled.ipynb ├── 使用f1_score计算macro_f1和micro_f1的值.ipynb ├── 使用train_test_split实现数据集的分割.ipynb ├── 实现交叉验证.ipynb ├── 计算recall_precision_f1值.ipynb ├── 计算每个类别下的f1值.ipynb └── 采样技术.ipynb ├── spacy └── Untitled.ipynb ├── torchtext ├── .data │ └── multi30k │ │ ├── mmt_task1_test2016.tar.gz │ │ ├── test2016.de │ │ ├── test2016.en 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