├── .gitignore ├── README.md ├── ToDo.md ├── assets ├── TIM图片20180811181406.png ├── TIM图片20180811182142.png ├── TIM截图20180608171710.png ├── TIM截图20180608172312.png ├── TIM截图20180608195851.png ├── TIM截图20180608204913.png ├── TIM截图20180608205223.png ├── TIM截图20180608212808.png ├── TIM截图20180610131620.png ├── TIM截图20180610132602.png ├── TIM截图20180610194353.png ├── TIM截图20180610203703.png ├── TIM截图20180610210251.png ├── TIM截图20180611150734.png ├── TIM截图20180611152559.png ├── TIM截图20180611171103.png ├── TIM截图20180611171213.png ├── TIM截图20180611172657.png ├── TIM截图20180611194754.png ├── TIM截图20180611195027.png ├── TIM截图20180611203427.png ├── TIM截图20180611204215.png ├── TIM截图20180611204503.png ├── TIM截图20180611211649.png ├── TIM截图20180611211753.png ├── TIM截图20180611214508.png ├── TIM截图20180611215422.png ├── TIM截图20180611215923.png ├── TIM截图20180611220109.png ├── TIM截图20180612104740.png ├── TIM截图20180612120851.png ├── TIM截图20180612121301.png ├── TIM截图20180612154333.png ├── TIM截图20180612213144.png ├── TIM截图20180613101802.png ├── TIM截图20180613150111.png ├── TIM截图20180613150711.png ├── TIM截图20180613151014.png ├── TIM截图20180613151356.png ├── TIM截图20180613200023.png ├── TIM截图20180613211935.png ├── TIM截图20180614103628.png ├── TIM截图20180614104032.png ├── TIM截图20180614111241.png ├── TIM截图20180614111712.png ├── TIM截图20180614203447.png ├── TIM截图20180615111903.png ├── TIM截图20180615150955.png ├── TIM截图20180617140439.png ├── TIM截图20180617141056.png ├── TIM截图20180617141502.png ├── TIM截图20180619104551.png ├── TIM截图20180619104649.png ├── TIM截图20180619104914.png ├── TIM截图20180619113501.png ├── TIM截图20180619114844.png ├── TIM截图20180619193332.png ├── TIM截图20180619193540.png ├── TIM截图20180619205103.png ├── TIM截图20180619214629.png ├── TIM截图20180619214735.png ├── TIM截图20180620164337.png ├── TIM截图20180620171300.png ├── TIM截图20180620171915.png ├── TIM截图20180620190555.png ├── TIM截图20180620191137.png ├── TIM截图20180620211714.png ├── TIM截图20180625113016.png ├── 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TIM截图20180628110920.png ├── TIM截图20180628120153.png ├── TIM截图20180628120634.png ├── TIM截图20180628121250.png ├── TIM截图20180628201027.png ├── TIM截图20180628201344.png ├── TIM截图20180628203925.png ├── TIM截图20180628205802.png ├── TIM截图20180628211047.png ├── TIM截图20180629094906.png ├── TIM截图20180629100103.png ├── TIM截图20180629101348.png ├── TIM截图20180629102412.png ├── TIM截图20180629114258.png ├── TIM截图20180629114604.png ├── TIM截图20180629150107.png ├── TIM截图20180629150441.png ├── TIM截图20180629150541.png ├── TIM截图20180629150705.png ├── TIM截图20180629150816.png ├── TIM截图20180629151001.png ├── TIM截图20180629152708.png ├── TIM截图20180629153515.png ├── TIM截图20180629155530.png ├── TIM截图20180629160418.png ├── TIM截图20180629164908.png ├── TIM截图20180629165317.png ├── TIM截图20180629193011.png ├── TIM截图20180629210220.png ├── TIM截图20180629210933.png ├── TIM截图20180629211550.png ├── TIM截图20180630094515.png ├── TIM截图20180630094555.png ├── TIM截图20180630094715.png ├── TIM截图20180630094826.png ├── 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TIM截图20180702150309.png ├── TIM截图20180702151423.png ├── TIM截图20180702152216.png ├── TIM截图20180702155142.png ├── TIM截图20180702171146.png ├── TIM截图20180702194825.png ├── TIM截图20180702195301.png ├── TIM截图20180702195902.png ├── TIM截图20180702201513.png ├── TIM截图20180702202810.png ├── TIM截图20180702203041.png ├── TIM截图20180702203401.png ├── TIM截图20180702204047.png ├── TIM截图20180702210202.png ├── TIM截图20180703102940.png ├── TIM截图20180703103735.png ├── TIM截图20180703103757.png ├── TIM截图20180703104845.png ├── TIM截图20180703105249.png ├── TIM截图20180703115823.png ├── TIM截图20180703115843.png ├── TIM截图20180703121303.png ├── TIM截图20180703143800.png ├── TIM截图20180703210401.png ├── TIM截图20180703210941.png ├── TIM截图20180704095607.png ├── TIM截图20180704111141.png ├── TIM截图20180704111911.png ├── TIM截图20180704112122.png ├── TIM截图20180704112743.png ├── TIM截图20180704161115.png ├── TIM截图20180704164022.png ├── TIM截图20180704165624.png ├── TIM截图20180704172524.png ├── TIM截图20180704174134.png ├── 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TIM截图20180801114036.png ├── TIM截图20180801114059.png ├── TIM截图20180801150958.png ├── TIM截图20180803225602.png ├── TIM截图20180805212441.png ├── TIM截图20180805231056.png ├── TIM截图20180805234123.png ├── TIM截图20180806141616.png ├── TIM截图20180806144052.png ├── TIM截图20180806151406.png ├── TIM截图20180806161221.png ├── TIM截图20180806165750.png ├── TIM截图20180806172158.png ├── TIM截图20180807094759.png ├── TIM截图20180807144435.png ├── TIM截图20180807151212.png ├── TIM截图20180807152158.png ├── TIM截图20180807152241.png ├── TIM截图20180807152306.png ├── TIM截图20180807152431.png ├── TIM截图20180807152459.png ├── TIM截图20180807152530.png ├── TIM截图20180807203305.png ├── TIM截图20180807210029.png ├── TIM截图20180807210133.png ├── TIM截图20180808105242.png ├── TIM截图20180808110903.png ├── TIM截图20180808111835.png ├── TIM截图20180808112920.png ├── TIM截图20180808114753.png ├── TIM截图20180808135512.png ├── TIM截图20180808232139.png ├── TIM截图20180812100103.png ├── TIM截图20180812100122.png ├── TIM截图20180812100344.png ├── TIM截图20180812100356.png ├── TIM截图20180812100416.png ├── TIM截图20180812100436.png ├── TIM截图20180812100503.png ├── TIM截图20180812100524.png ├── TIM截图20180812100550.png ├── TIM截图20180812100606.png ├── TIM截图20180812100617.png ├── arbitrary_padding_no_strides.gif ├── arbitrary_padding_no_strides_transposed.gif ├── confusion_matrix.png ├── conv_dilation.gif ├── conv_no_padding_no_strides_transposed.gif ├── dilation.gif ├── full_padding_no_strides.gif ├── full_padding_no_strides_transposed.gif ├── no_padding_no_strides.gif ├── no_padding_no_strides_transposed.gif ├── no_padding_strides.gif ├── no_padding_strides_transposed.gif ├── padding_strides.gif ├── padding_strides_odd.gif ├── padding_strides_odd_transposed.gif ├── padding_strides_transposed.gif ├── same_padding_no_strides.gif ├── same_padding_no_strides_transposed.gif ├── 公式_20180610212719.png ├── 公式_20180610213145.png ├── 公式_20180610213218.png ├── 公式_20180610213257.png ├── 公式_20180610213349.png ├── 公式_20180610213428.png ├── 公式_20180610213451.png ├── 公式_20180610214123.png ├── 公式_20180610214502.png ├── 公式_20180610214846.png ├── 公式_20180610214926.png ├── 公式_20180610215029.png ├── 公式_20180610215150.png ├── 公式_20180610215222.png ├── 公式_20180610215308.png ├── 公式_20180610215339.png ├── 公式_20180610215417.png ├── 公式_20180610215445.png ├── 公式_20180610215522.png ├── 公式_20180610215554.png ├── 公式_20180610215623.png ├── 公式_20180610215656.png ├── 公式_20180610215732.png ├── 公式_20180610215812.png ├── 公式_20180611194550.png ├── 公式_20180611194704.png ├── 公式_20180611195622.png ├── 公式_20180611210928.png ├── 公式_20180613164438.png ├── 公式_20180613164637.png ├── 公式_20180613170734.png ├── 公式_20180613171757.png ├── 公式_20180613201829.png ├── 公式_20180613210141.png ├── 公式_20180613211004.png ├── 公式_20180613211437.png ├── 公式_20180620160408.png ├── 公式_20180620160538.png ├── 公式_20180620204006.png ├── 公式_20180620205055.png ├── 公式_20180620210025.png ├── 公式_20180620213601.png ├── 公式_20180624211704.png ├── 公式_20180625144700.png ├── 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README.md ├── 微积分的本质.md └── 深度学习的核心.md ├── 机器学习-深度学习-NLP ├── DL-CNN.md ├── DL-RNN.md ├── DL-《深度学习》整理.md ├── DL-深度学习基础.md ├── DL-深度学习实践.md ├── ML-机器学习实践.md ├── ML-机器学习算法.md ├── NLP-中文分词.md ├── NLP-序列建模.md ├── NLP-自然语言处理基础.md ├── NLP-自然语言处理实践.md ├── NLP-词向量.md ├── NLP-问答模型.md ├── README.md ├── ToDo.md ├── 专题-Bash命令.md ├── 专题-工具库.md ├── 专题-损失函数.md └── 专题-背景知识.md ├── 笔试面经 ├── README.md └── 笔试-网易.md ├── 算法 ├── README.md ├── README_bak.md ├── 专题-位运算.md ├── 专题-动态规划.md ├── 专题-大数运算.md ├── 专题-局部敏感哈希.md ├── 专题-海量数据处理.md ├── 专题-经典算法问题.md ├── 备忘-IO模板.md ├── 备忘-必备算法.md ├── 题解-LeetCode.md └── 题解-剑指Offer.md └── 编程语言 ├── Cpp-基础知识.md ├── Cpp-左值与右值.md ├── Cpp-面向对象编程.md └── README.md /.gitignore: -------------------------------------------------------------------------------- 1 | # IDE 2 | .idea/ 3 | .vscode/ 4 | 5 | # folder 6 | __*/ 7 | .ipynb_checkpoints/ 8 | out/ 9 | 10 | # file 11 | *.zip 12 | *.tgz 13 | Untitled.ipynb 14 | MyCV.pdf 15 | 16 | # root 17 | /demo.py 18 | /test.py 19 | 20 | # prevent being filtered 21 | !__init__.py 22 | 23 | # Python 24 | .py[cod] 25 | *.egg 26 | *.egg-info 27 | /dist/ 28 | __pycache__/ 29 | -------------------------------------------------------------------------------- /ToDo.md: -------------------------------------------------------------------------------- 1 | - 规则系统 2 | - 自动机 3 | - 正则表达式 4 | - 中文分词 5 | - NLP 技术 6 | - NLP 实践 7 | - 模板匹配 + 分类器 8 | - 给一个应用，给出可能用到的技术 9 | - NLP 的难点 10 | - 领域隔离 11 | - 语言可以描述任何领域 12 | - 任何跨领域的 NLP 都困难重 13 | - 知识库 14 | - 语言本身就是最好的表示 - 搜索引擎 15 | - 标注数据 16 | - 众包 17 | - 评价指标 18 | - seq2seq 模型提速 19 | - 解决 NLP 问题的一般思路 20 | - 这个问题人能做好吗？ 21 | - 可以 - 设计流程让计算机模型人的思路 22 | - 很难 - 尝试从计算机的角度来思考问题 23 | 24 | - Bash Script 25 | - wc/sed/awk/grep/sort/uniq/paste/cat/head/tail 26 | - 一个很大的txt，30s内找出出现次数最多的前10个词汇 27 | - 查看第30行到第40行的数据 28 | 29 | - Stanford Core NLP 30 | - 语义分析 31 | - NLTK 32 | - 句子划分 33 | - 读取语义树 34 | - spacy 35 | - 超参数搜索 36 | - Seq2Seq 模型可视化 37 | - Hidden States 可视化 38 | - Word Embedding 可视化 39 | - 降维 - PCA、t-SNE 40 | 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case ns_square: 22 | _pmf = func_tbl[nst]; 23 | break; 24 | default: 25 | cerr << "invalid sequence type\n"; 26 | } 27 | } 28 | 29 | void print(int n) { 30 | (this->*_pmf)(n); // 通过指针选择需要调用的函数 31 | } 32 | 33 | // _pmf 可以指向以下任何一个函数 34 | void fibonacci(int n) { 35 | int f = 1; 36 | int g = 1; 37 | for (int i = 2; i <= n; i++) 38 | g = g + f, f = g - f; 39 | cout << f << endl; 40 | } 41 | 42 | void square(int n) { 43 | cout << n * n << endl; 44 | } 45 | 46 | private: 47 | PtrType _pmf; 48 | static PtrType func_tbl[cnt_seq]; // 保存所有序列函数的指针 49 | // 为了兼容性，不推荐写成 `static vector（没有空格）> _seq;` 50 | }; 51 | 52 | // static 成员变量初始化 53 | num_sequence::PtrType 54 | num_sequence::func_tbl[cnt_seq] = { 55 | &num_sequence::fibonacci, 56 | &num_sequence::square, 57 | }; 58 | 59 | int main() { 60 | 61 | auto ns = num_sequence(); 62 | ns.print(5); // 5 63 | ns.set_sequence(num_sequence::ns_square); // 调整函数指针以获得多态的效果 64 | ns.print(5); // 25 65 | 66 | cout << endl; 67 | system("PAUSE"); 68 | return 0; 69 | } 70 | 71 | -------------------------------------------------------------------------------- /code/model/倒排索引/data/a.txt: -------------------------------------------------------------------------------- 1 | a b cb,f h f ds -------------------------------------------------------------------------------- /code/model/倒排索引/data/b.txt: -------------------------------------------------------------------------------- 1 | html a b c -------------------------------------------------------------------------------- /code/model/倒排索引/data/c.txt: -------------------------------------------------------------------------------- 1 | abc h html -------------------------------------------------------------------------------- /code/my_tensorflow/demo.py: -------------------------------------------------------------------------------- 1 | from typing import ( 2 | TypeVar, Iterator, Iterable, overload, Container, 3 | Sequence, MutableSequence, Mapping, MutableMapping, Tuple, List, Any, Dict, Callable, Generic, 4 | Set, AbstractSet, FrozenSet, MutableSet, Sized, Reversible, SupportsInt, SupportsFloat, 5 | SupportsBytes, SupportsAbs, SupportsRound, IO, Union, ItemsView, KeysView, ValuesView, 6 | ByteString, Optional, AnyStr, Type, 7 | ) 8 | from src.utils import tf_dtype 9 | 10 | print(tf_dtype) 11 | 12 | import tensorflow as tf 13 | import numpy as np 14 | 15 | from src.layers import multi_dense, Dense 16 | 17 | from tensorlayer.layers import DenseLayer, Conv2dLayer 18 | 19 | from keras.layers import Dense, Conv2D 20 | 21 | import keras.initializers 22 | 23 | import keras.utils.layer_utils 24 | 25 | x = tf.constant(np.arange(16).reshape([8, 2]), dtype=tf.float32) 26 | 27 | o = multi_dense(x, [3, 4, 5], name="DenSe") 28 | 29 | # sess = tf.Session(graph=tf.get_default_graph()) 30 | tf.reset_default_graph() 31 | sess = tf.InteractiveSession() 32 | 33 | sess.run(tf.global_variables_initializer()) 34 | 35 | sess.run(o) 36 | 37 | 38 | def f(x): 39 | """ 40 | 41 | Args: 42 | x(function): 43 | 44 | Returns: 45 | 46 | """ 47 | 48 | 49 | def b(): 50 | """""" 51 | 52 | 53 | f(1) 54 | 55 | sorted() 56 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/code/my_tensorflow/src/__init__.py -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/__init__.py: -------------------------------------------------------------------------------- 1 | """激活函数""" 2 | 3 | from .relu import * 4 | from .selu import * 5 | from .softmax import * 6 | 7 | 8 | def linear(x): 9 | """""" 10 | return x 11 | 12 | 13 | def identity(x): 14 | """""" 15 | return tf.identity(x) 16 | 17 | 18 | def sigmoid(x): 19 | """""" 20 | return tf.nn.sigmoid(x) 21 | 22 | 23 | def hard_sigmoid(x): 24 | """ 25 | x = 0. x < -2.5 26 | = 1. x > 2.5 27 | = 0.2 * x + 0.5 otherwise 28 | """ 29 | x = (0.2 * x) + 0.5 30 | x = tf.clip_by_value(x, 0., 1.) 31 | return x 32 | 33 | 34 | def tanh(x): 35 | return tf.nn.tanh(x) 36 | 37 | 38 | def softplus(x): 39 | return tf.nn.softplus(x) 40 | 41 | 42 | def softsign(x): 43 | """ 44 | o = x / (1 + abs(x)) 45 | """ 46 | return tf.nn.softsign(x) 47 | 48 | 49 | def softmax(x): 50 | """""" 51 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/relu.py: -------------------------------------------------------------------------------- 1 | """ReLU 系列激活函数""" 2 | import tensorflow as tf 3 | 4 | 5 | def relu(x): 6 | """""" 7 | return tf.nn.relu(x) 8 | 9 | 10 | def leaky_relu(x, alpha=0.2): 11 | """渗透 ReLU""" 12 | return tf.nn.leaky_relu(x, alpha) 13 | 14 | 15 | def relu_clip(x, max_value): 16 | """""" 17 | o = tf.nn.relu(x) 18 | o = tf.minimum(o, max_value) 19 | return o 20 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/selu.py: -------------------------------------------------------------------------------- 1 | """指数线性单元""" 2 | 3 | import tensorflow as tf 4 | 5 | 6 | def elu(x): 7 | """指数线性单元""" 8 | return tf.nn.elu(x) 9 | 10 | 11 | def selu(x): 12 | """缩放型指数线性单元""" 13 | alpha = 1.6732632423543772848170429916717 14 | scale = 1.0507009873554804934193349852946 15 | o = tf.nn.elu(x) 16 | return scale * tf.where(x > 0, o, alpha * o) 17 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/activations/softmax.py: -------------------------------------------------------------------------------- 1 | """SoftMax""" 2 | 3 | import tensorflow as tf 4 | 5 | 6 | def softmax(x, axis=-1): 7 | """ 8 | Args: 9 | x(tf.Tensor): 10 | axis: 11 | """ 12 | n_dim = x.get_shape().ndims 13 | assert n_dim >= 2, "Cannot apply softmax to a tensor that is 1D" 14 | 15 | if n_dim == 2: 16 | return tf.nn.softmax(x) 17 | else: 18 | e = tf.exp(x - tf.reduce_max(x, axis=axis, keepdims=True)) 19 | s = tf.reduce_sum(e, axis=axis, keepdims=True) 20 | return e / s 21 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/initializers/__init__.py: -------------------------------------------------------------------------------- 1 | """变量初始化器""" 2 | 3 | import tensorflow as tf 4 | 5 | 6 | '''常用的 Tensorflow 基本初始化器''' 7 | # 零 8 | zeros = tf.initializers.zeros 9 | # 常量 10 | constant = tf.initializers.constant 11 | # 标准正太分布 12 | normal = tf.initializers.random_normal 13 | # 截断正态分布 14 | truncated_normal = tf.initializers.truncated_normal 15 | # 均匀分布 16 | uniform = tf.initializers.random_uniform 17 | # 18 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/__init__.py: -------------------------------------------------------------------------------- 1 | """常见层与常用层的写法 2 | """ 3 | 4 | from .dense import * 5 | from .cnn import * 6 | from .highway import * 7 | 8 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/cnn.py: -------------------------------------------------------------------------------- 1 | """卷积层 2 | 3 | References: 4 | tensorlayer.layers.Conv2dLayer 5 | """ 6 | import tensorflow as tf 7 | 8 | from ..activations import relu 9 | from ..utils import get_wb 10 | 11 | 12 | def conv2d(x, kernel_size, out_channels, 13 | act_fn=relu, 14 | strides=1, 15 | padding="SAME", 16 | name=None, 17 | reuse=False): 18 | """2-D 卷积层 19 | Input shape: [batch_size, in_w, in_h, in_channels] 20 | Output shape: [batch_size, out_w, out_h, out_channels] 21 | 22 | Args: 23 | x(tf.Tensor): 24 | kernel_size(int or list of int): 25 | out_channels(int): 26 | act_fn(function): 27 | strides(int or list of int): 28 | padding(str): 29 | name(str): 30 | reuse(bool): 31 | 32 | Returns: 33 | 34 | """ 35 | if isinstance(kernel_size, int): 36 | kernel_size = [kernel_size] * 2 37 | if isinstance(strides, int): 38 | strides = [strides] * 4 39 | 40 | assert len(kernel_size) == 2 41 | assert len(strides) == 4 42 | 43 | in_channels = int(x.get_shape()[-1]) 44 | kernel_shape = list(kernel_size) + [in_channels, out_channels] 45 | 46 | with tf.variable_scope(name or "conv2d", reuse=reuse): 47 | W, b = get_wb(kernel_shape) 48 | 49 | o = tf.nn.conv2d(x, W, strides=strides, padding=padding) + b 50 | o = act_fn(o) 51 | 52 | return o 53 | 54 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/dense.py: -------------------------------------------------------------------------------- 1 | """ 2 | 全连接层 3 | 4 | References: 5 | tensorlayer.layers.DenseLayer 6 | keras.layers.Dense 7 | """ 8 | import tensorflow as tf 9 | 10 | from ..utils import get_wb 11 | from ..activations import relu 12 | from ..activations import linear 13 | 14 | 15 | def dense(x, n_unit, act_fn=relu, name=None, reuse=False): 16 | """全连接层 17 | Input shape: [batch_size, n_input] 18 | Output shape: [batch_size, n_unit] 19 | 20 | 如果需要 reuse 推荐使用类实现的 `Dense` 21 | 22 | Args: 23 | x(tf.Tensor): 24 | n_unit(int): 25 | act_fn: 26 | name(str): 27 | reuse(bool): 28 | """ 29 | # n_input = tf.shape(x)[-1] # err: need int but tensor 30 | n_input = int(x.get_shape()[-1]) 31 | with tf.variable_scope(name or "dense", reuse=reuse): 32 | W, b = get_wb([n_input, n_unit]) 33 | o = act_fn(tf.matmul(x, W) + b) 34 | return o 35 | 36 | 37 | def multi_dense(x, n_unit_ls, act_fn=relu, name=None): 38 | """多层全连接 39 | Input shape: [batch_size, n_input] 40 | Output shape: [batch_size, n_unit_list[-1]] 41 | 42 | Args: 43 | x(tf.Tensor): 44 | n_unit_ls(list of int): 45 | act_fn: 46 | name(str): 47 | """ 48 | # n_layer = len(n_unit_list) 49 | name = name or "dense" 50 | for i, n_unit in enumerate(n_unit_ls): 51 | x = dense(x, n_unit, act_fn=act_fn, name="{}-{}".format(name, i)) 52 | 53 | return x 54 | 55 | 56 | def linear_dense(x, n_unit, name=None, reuse=False): 57 | """线性全连接层 58 | Input shape: [batch_size, n_input] 59 | Output shape: [batch_size, n_unit] 60 | 61 | Args: 62 | x(tf.Tensor): 63 | n_unit(int): 64 | name(str): 65 | reuse(bool) 66 | """ 67 | return dense(x, n_unit, act_fn=linear, name=(name or "linear_dense"), reuse=reuse) 68 | 69 | 70 | class Dense(object): 71 | """全连接层的类实现，便于 reuse""" 72 | 73 | def __init__(self, n_unit, act_fn=relu, name=None): 74 | """""" 75 | self.n_unit = n_unit 76 | self.act_fn = act_fn 77 | self.name = name 78 | 79 | self._built = False 80 | 81 | def _build(self, n_input): 82 | """""" 83 | with tf.variable_scope(self.name or "dense"): 84 | self.W, self.b = get_wb([n_input, self.n_unit]) 85 | 86 | def _call(self, x): 87 | """""" 88 | o = self.act_fn(tf.matmul(x, self.W) + self.b) 89 | return o 90 | 91 | def __call__(self, x): 92 | """""" 93 | n_input = int(x.get_shape()[-1]) 94 | if not self._built: 95 | self._build(n_input) 96 | self._built = True 97 | 98 | return self._call(x) 99 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/layers/highway.py: -------------------------------------------------------------------------------- 1 | """高速网络 Highway Network 2 | 3 | 注意 x 经过 Highway 之后维度应该保持不变 4 | 5 | References: 6 | https://github.com/fomorians/highway-fcn 7 | """ 8 | import tensorflow as tf 9 | 10 | from ..utils import get_wb 11 | from ..activations import relu, sigmoid 12 | 13 | 14 | def highway_dense(x, act_fn=relu, carry_bias=-1.0, name=None): 15 | """用于全连接层的 highway 16 | Input shape: [batch_size, n_input] 17 | Output shape: [batch_size, n_input] 18 | 19 | 公式 20 | `o = H(x, W)T(x, W) + x(1 - T(x, W))` 21 | """ 22 | n_input = int(x.get_shape()[-1]) 23 | with tf.variable_scope(name or "highway_dense"): 24 | W, b = get_wb([n_input, n_input]) 25 | 26 | with tf.variable_scope("transform"): 27 | W_T, b_T = get_wb([n_input, n_input], b_initializer=tf.initializers.constant(carry_bias)) 28 | 29 | H = act_fn(tf.matmul(x, W) + b) 30 | T = sigmoid(tf.matmul(x, W_T) + b_T) 31 | o = tf.multiply(H, T) + tf.multiply(x, (1. - T)) 32 | 33 | return o 34 | 35 | 36 | def multi_highway_dense(x, n_layer, act_fn=relu, carry_bias=-1.0, name=None): 37 | """多层 highway_dense 38 | Input shape: [batch_size, n_input] 39 | Output shape: [batch_size, n_input] 40 | """ 41 | name = name or "highway_dense" 42 | for i in range(n_layer): 43 | x = highway_dense(x, act_fn=act_fn, carry_bias=carry_bias, name="{}-{}".format(name, i)) 44 | 45 | return x 46 | 47 | 48 | def highway_conv2d(x, kernel_size, 49 | carry_bias=-1.0, 50 | act_fn=relu, 51 | strides=1, 52 | padding="SAME", 53 | name=None): 54 | """用于 conv2d 的 highway 55 | Input shape: [batch_size, in_w, in_h, in_channels] 56 | Output shape: [batch_size, in_w, in_w, in_channels] 57 | 58 | 公式 59 | `o = H(x, W)T(x, W) + x(1 - T(x, W))` 60 | """ 61 | if isinstance(kernel_size, int): 62 | kernel_size = [kernel_size] * 2 63 | if isinstance(strides, int): 64 | strides = [strides] * 4 65 | 66 | assert len(kernel_size) == 2 67 | assert len(strides) == 4 68 | 69 | in_channels = int(x.get_shape()[-1]) 70 | kernel_shape = list(kernel_size) + [in_channels, in_channels] 71 | 72 | with tf.variable_scope(name or "highway_conv2d"): 73 | W, b = get_wb(kernel_shape, b_initializer=tf.initializers.constant(carry_bias)) 74 | 75 | with tf.variable_scope("transform"): 76 | W_T, b_T = get_wb(kernel_shape) 77 | 78 | H = act_fn(tf.nn.conv2d(x, W, strides=strides, padding=padding) + b) 79 | T = sigmoid(tf.nn.conv2d(x, W_T, strides=strides, padding=padding) + b_T) 80 | o = tf.multiply(H, T) + tf.multiply(x, (1. - T)) 81 | return o 82 | 83 | 84 | # TODO(huay): 因为卷积的参数比较复杂，如果需要多层 highway_conv2d，不如单独设置参数 85 | # def multi_highway_conv2d(): 86 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/regularizers/L1L2.py: -------------------------------------------------------------------------------- 1 | """ 2 | L1 和 L2 正则化 3 | 4 | References: 5 | keras.regularizers 6 | """ 7 | import tensorflow as tf 8 | import numpy as np 9 | 10 | 11 | class L1L2Regularizer(object): 12 | """L1 L2 正则化 13 | 14 | Examples: 15 | l2_regularizer = l2(0.01) 16 | tf.get_variable(..., regularizer=l2_regularizer, ...) 17 | """ 18 | 19 | def __init__(self, l1=0., l2=0.): 20 | """ 21 | Args: 22 | l1(float): L1 正则化的系数 23 | l2(float): L2 正则化的系数 24 | """ 25 | self.l1 = np.asarray(l1, dtype=np.float32) 26 | self.l2 = np.asarray(l2, dtype=np.float32) 27 | 28 | def __call__(self, x): 29 | """ 30 | Args: 31 | x: 注意 x.dtype == float32 32 | """ 33 | # x = tf.cast(x, dtype=tf.float32) # 交给外部处理 34 | loss_regularization = 0. 35 | if self.l1: 36 | loss_regularization += tf.reduce_sum(self.l1 * tf.abs(x)) 37 | if self.l2: 38 | loss_regularization += tf.reduce_sum(self.l2 * tf.square(x)) 39 | return loss_regularization 40 | 41 | 42 | """预定义好的正则化器 43 | """ 44 | l1_regularizer = L1L2Regularizer(l1=0.01) 45 | 46 | l2_regularizer = L1L2Regularizer(l2=0.01) 47 | 48 | l1_l2_regularizer = L1L2Regularizer(l1=0.01, l2=0.01) 49 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/regularizers/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 正则化函数 3 | `Tensor -> Tensor or None` 4 | 5 | Examples: 6 | l2_regularizer = l2(0.01) 7 | tf.get_variable(..., regularizer=l2_regularizer, ...) 8 | """ 9 | from .L1L2 import * 10 | -------------------------------------------------------------------------------- /code/my_tensorflow/src/utils/__init__.py: -------------------------------------------------------------------------------- 1 | """ 2 | 工具函数 3 | """ 4 | from pprint import pprint 5 | from functools import reduce 6 | from operator import mul 7 | 8 | import tensorflow as tf 9 | from ..regularizers import l2_regularizer 10 | 11 | tf_dtype = tf.float32 12 | zeros = tf.initializers.zeros 13 | truncated_normal = tf.initializers.truncated_normal 14 | 15 | 16 | def foo(): 17 | print(1) 18 | 19 | 20 | def get_wb(shape, 21 | w_initializer=truncated_normal, 22 | b_initializer=zeros, 23 | w_regularizer=l2_regularizer, 24 | b_regularizer=l2_regularizer): 25 | """""" 26 | W = tf.get_variable('W', shape=shape, 27 | dtype=tf_dtype, initializer=w_initializer, regularizer=w_regularizer) 28 | b = tf.get_variable('b', shape=shape[-1:], 29 | dtype=tf_dtype, initializer=b_initializer, regularizer=b_regularizer) 30 | return W, b 31 | 32 | 33 | def get_w(shape, 34 | w_initializer=truncated_normal, 35 | w_regularizer=l2_regularizer): 36 | n_in, n_unit = shape 37 | W = tf.get_variable('W', [n_in, n_unit], dtype=tf_dtype, initializer=w_initializer, regularizer=w_regularizer) 38 | return W 39 | 40 | 41 | def get_params_dict(): 42 | """以字典形式获取所有 trainable 参数""" 43 | param_dict = dict() 44 | for var in tf.trainable_variables(): 45 | param_dict[var.name] = {"shape": list(map(int, var.shape)), 46 | "number": int(reduce(mul, var.shape, 1))} 47 | return param_dict 48 | 49 | 50 | def print_params_dict(): 51 | """""" 52 | param_dict = get_params_dict() 53 | # pprint(param_dict, indent=2) 54 | for k, v in param_dict.items(): 55 | print(k, '\t', end='') 56 | pprint(v, indent=2) 57 | # for vk, vv in v.items(): 58 | # print(vk, '-', vv, '\t', end='') 59 | # print() 60 | 61 | 62 | def get_params_number(): 63 | """获取参数总量""" 64 | param_dict = get_params_dict() 65 | return sum((item["number"] for item in param_dict.values())) 66 | 67 | 68 | def print_params_number(): 69 | """""" 70 | print(get_params_number()) 71 | -------------------------------------------------------------------------------- /papers/[1505]-Highway.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/papers/[1505]-Highway.pdf -------------------------------------------------------------------------------- /papers/[1509]-CharCNN.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/papers/[1509]-CharCNN.pdf -------------------------------------------------------------------------------- /papers/[1611]-BiDAF.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/papers/[1611]-BiDAF.pdf -------------------------------------------------------------------------------- /数学/README.md: -------------------------------------------------------------------------------- 1 | **知识回顾** 2 | --- 3 | - [微积分的本质](./微积分的本质.md) 4 | - [深度学习的核心-梯度下降与反向传播](./深度学习的核心.md) 5 | 6 | **数学问题** 7 | --- 8 | 9 | 10 | - [1. 在圆环上随机选取 3 个点，这 3 个点组成锐角三角形的概率](#1-在圆环上随机选取-3-个点这-3-个点组成锐角三角形的概率) 11 | 12 | 13 | 14 | 15 | # 1. 在圆环上随机选取 3 个点，这 3 个点组成锐角三角形的概率 16 | > 今日头条-算法工程师-实习 17 | 18 | - 答案：1/4 19 | 20 | - 简单回答：易知，当A、B、C三点都在同一个半圆内时，三角形ABC必是直角或钝角三角形；只有当三点不在同一个半圆内，才可以组成锐角三角形。于是问题等价于“在圆周上任取三个不同的点，求它们不在同一半圆内的概率”，故其概率为 `1/2 * 1/2 = 1/4`（先固定点 A，B 和 C 分别与之在同一个半圆内的概率为 1/2） 21 | > [在一圆周上任意取三个点构成锐角三角形的概率是多少?](https://www.zybang.com/question/7ad30f46d6eebb125b6421479d24bdc3.html) - 作业帮 22 | 23 | - 使用积分计算该概率 24 | 25 | [![](../assets/公式_20180624211704.png)](http://www.codecogs.com/eqnedit.php?latex=\int_{0}^{\pi}\frac{\theta&space;}{2\pi}\cdot&space;\frac{1}{\pi}=\frac{\theta^2}{4\pi^2}\Big|^\pi_0=\frac{1}{4}) 26 | > [在一圆周上任意取三个点构成锐角三角形的概率是多少](https://zhidao.baidu.com/question/1884315387170029428.html) - 百度知道 27 | 28 | > [【官方双语】如何优雅地解答最难数学竞赛的压轴题？ 1:50-4:25, 5:40-7:50](https://www.bilibili.com/video/av17275211) - bilibili 29 | -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/DL-RNN.md: -------------------------------------------------------------------------------- 1 | DL-RNN 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [RNN 的几种设计模型（3）](#rnn-的几种设计模型3) 9 | 10 | 11 | 12 | ## RNN 的几种设计模型（3） 13 | > DL-《深度学习整理》#[RNN 的几种基本设计模式](#rnn-的几种基本设计模式) -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/DL-深度学习实践.md: -------------------------------------------------------------------------------- 1 | DL-深度学习实践 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [Batch Normalization 批正则化 TODO](#batch-normalization-批正则化-todo) 9 | 10 | 11 | 12 | ## Batch Normalization 批正则化 TODO -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-中文分词.md: -------------------------------------------------------------------------------- 1 | **RoadMap** 2 | --- 3 | 4 | 5 | # 中文分词 6 | 7 | ## Reference 8 | > [漫话中文自动分词和语义识别（上）：中文分词算法](http://www.matrix67.com/blog/archives/4212) | Matrix67 9 | - 中文分词的**难点** 10 | - **交集型歧义** 11 | - **组合型歧义** 12 | - **未登录词** 13 | - 人名 14 | - 地名 15 | - 机构名 16 | - 缩略语 17 | - 网络用语 18 | - 中文分词的历史 19 | - 规则表/词典——**前缀树** 20 | - 最长匹配 21 | - 最少词数 22 | - 惩罚方法 23 | - 基于有向无环图的最大概率组合 24 | - 基于语言模型的最大概率组合 25 | - 字标注 26 | - HMM 27 | - CRF 28 | > jieba 分词就同时结合了两者：有向图的最大概率 + HMM 模型 29 | 30 | ## 基于 AC 自动机的中文分词 31 | > [【中文分词系列】 1. 基于AC自动机的快速分词](https://kexue.fm/archives/3908) - 科学空间|Scientific Spaces 32 | -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-自然语言处理实践.md: -------------------------------------------------------------------------------- 1 | NLP-自然语言处理实践 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | ## NLP 怎么做数据增强 9 | - 数据增强，具体来说就是对原数据集进行一定程度的改动，从而得到 10 | 11 | ### 双向翻译 12 | - 利用 NMT 做双向翻译——将语言A 翻译到其他语言，再翻译回语言 A
13 | 这个过程相当于对样本进行了改写，使得训练样本的数量大大增加 14 | - QANet 中的做法： 15 |
16 | 17 | - 对材料中每个句子通过翻译引擎得到`k`句法语候选，然后将每句法语转回英语，得到`k^2`个改写的句子，从中随机选择一句作为 18 | - 改写后答案的位置也可能改变，如何寻找**新答案的位置**？
19 | 具体到 SQuAD 任务就是 `(d,q,a) -> (d’, q, a’)`，问题不变，对文档 `d` 翻译改写，由于改写后原始答案 `a` 现在可能已经不在改写后的段落 `d’` 里了，所以需要从改写后的段落 `d’` 里抽取新的答案 `a’`，采用的方法是计算 `d’` 里每个单词和原始答案里 start/end words 之间的 **character-level 2-gram score**，分数最高的单词就被选择为新答案 `a’` 的 start/end word。 20 | > 中文没有里面没有 character-level 2-gram，可以考虑词向量之间的相似度 21 | 22 | ### 同义词替换 TODO 23 | - 图像中对图片进行缩放、平移、旋转等操作不会改变图片本身含义；语音中对语音的声调、语速、噪声稍加改动也不会改变其结果 24 | - 文本中能做的类似改动比较少，同义词替换算一种 25 | - 哪些词应当被替换 26 | - 应该是用哪个同义词来替换 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/NLP-问答模型.md: -------------------------------------------------------------------------------- 1 | NLP-问答模型 2 | === 3 | 4 | 问答模型简述 5 | --- 6 | - 问答模型可以概括为 Seq2Seq 模型，但通常会比较复杂 7 | > [SQuAD文本理解挑战赛十大模型解读](https://www.sohu.com/a/216468593_500659)_搜狐科技_搜狐网 8 | 9 | Index 10 | --- 11 | 12 | 13 | - [QANet 模型](#qanet-模型) 14 | - [BiDAF 模型](#bidaf-模型) 15 | - [模型结构](#模型结构) 16 | - [参数规模 TODO](#参数规模-todo) 17 | - [论文/代码/参考](#论文代码参考) 18 | - [decaNLP 模型](#decanlp-模型) 19 | - [论文/代码](#论文代码) 20 | 21 | 22 | 23 | ## QANet 模型 24 | 25 | ## BiDAF 模型 26 | 27 | ### 模型结构 28 | - 模型共 6 层 29 |
30 | 31 | - **嵌入层**（char + word） 32 | - 记**材料**（context）和**查询**（query）分别为 33 |
34 | 35 | - 词向量使用预训练的 GloVe，OOV 可训练 36 | - 字向量由 CharCNN 训练 37 | > NLP-词向量#[CharCNN](./NLP-词向量.md#charcnn-字向量) 38 | - CharCNN 负责将**每个单词**映射到一个**向量** 39 | - 实际为单词中每个**字符向量**的叠加平均，具体由一个**卷积层和池化层**实现 40 | - 字符嵌入层实际上生成的也是一个词向量，但是使用的字符信息，而不是上下文信息 41 | - 每个单词的表示由词向量和字向量拼接而成，然后经过两层 highway 得到 centext vector 42 | - **Word Embedding Layer** 43 | - 使用预训练好的 GloVe 词向量 44 | > NLP-词向量#[GloVe](./NLP-词向量.md#glove) 45 | - 默认词向量和字符向量的维度相同 46 | - **输入 Embedding** 由 Character Embedding 和 Word Embedding **拼接**而成 47 | - **Contextual Embedding Layer** 48 | - 利用周围词语的语境线索（contextual cues）来**改善**（refine）词的嵌入。 49 | - 双向 LSTM 50 | - **Attention Flow Layer** 51 | - 耦合（couple）材料与问题，并为上下文中的**每个词**生成一组**查询感知特征向量**（query-aware feature vectors） 52 | - **Modeling Layer** 53 | - 使用 RNN 扫描（scan）上下文 54 | - **Output Layer** 55 | - 产生问题的答案 56 | 57 | ### 参数规模 TODO 58 | 59 | 60 | ### 论文/代码/参考 61 | - [1611.01603] [Bidirectional Attention Flow for Machine Comprehension](https://arxiv.org/abs/1611.01603) 62 | - [allenai/bi-att-flow](https://github.com/allenai/bi-att-flow/tree/dev) (TF1.0+) - GitHub 63 | - BiDAF https://allenai.github.io/bi-att-flow/ 64 | - [论文笔记 - Bi-Directional Attention Flow for Machine Comprehension](http://www.shuang0420.com/2018/04/01/论文笔记%20-%20Bi-Directional%20Attention%20Flow%20for%20Machine%20Comprehension/) | 徐阿衡 65 | 66 | ## decaNLP 模型 67 | > [一个模型搞定十大自然语言任务](https://www.toutiao.com/i6569393480089469454)｜论文+代码 68 | 69 | ### 论文/代码 70 | - [1806.08730] [The Natural Language Decathlon: Multitask Learning as Question Answering](https://arxiv.org/abs/1806.08730) 71 | - [salesforce/decaNLP](https://github.com/salesforce/decaNLP) - GitHub 72 | -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/README.md: -------------------------------------------------------------------------------- 1 | 机器学习 & 深度学习 2 | === 3 | 4 | ## 公共基础 5 | - [背景知识](./专题-背景知识.md) 6 | - [损失函数](./专题-损失函数.md) 7 | 8 | ## 机器学习 9 | 10 | ## 深度学习 11 | 12 | ## 自然语言处理 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/ToDo.md: -------------------------------------------------------------------------------- 1 | ToDo list 2 | --- 3 | 4 | - [ ] -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-Bash命令.md: -------------------------------------------------------------------------------- 1 | 专题-Bash命令 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [Bash 必背命令](#bash-必背命令) 9 | - [wc/sed/awk/grep/sort/uniq/paste/cat/head/tail](#wcsedawkgrepsortuniqpastecatheadtail) 10 | - [---](#---) 11 | - [wc](#wc) 12 | - [使用场景](#使用场景) 13 | - [统计文件中出现次数最多的前 10 个词](#统计文件中出现次数最多的前-10-个词) 14 | 15 | 16 | 17 | # Bash 必背命令 18 | 19 | ## wc/sed/awk/grep/sort/uniq/paste/cat/head/tail 20 | 21 | ## --- 22 | 23 | ## wc 24 | 25 | # 使用场景 26 | ## 统计文件中出现次数最多的前 10 个词 27 | ```bash 28 | 29 | ``` -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-工具库.md: -------------------------------------------------------------------------------- 1 | 专题-工具库 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [工具列表](#工具列表) 9 | - [jieba 分词](#jieba-分词) 10 | - [分词](#分词) 11 | 12 | 13 | 14 | # 工具列表 15 | - Stanford Core NLP 16 | - 语义分析 17 | - NLTK 18 | - 分词（西文）、分句、读取语义树 19 | - 词干提取 20 | - jieba 21 | - 中文分词、词性标注 22 | 23 | # jieba 分词 24 | > fxsjy/[jieba: 结巴中文分词](https://github.com/fxsjy/jieba) 25 | 26 | ## 分词 27 | **代码示例** 28 | ```Python 29 | import jieba 30 | 31 | # 全模式 32 | seg_list = jieba.cut("我来到北京清华大学", cut_all=True) 33 | print("【全模式】: " + "/ ".join(seg_list)) 34 | 35 | # 精确模式（默认） 36 | seg_list = jieba.cut("我来到北京清华大学", cut_all=False) 37 | # seg_list = jieba.cut("他来到了网易杭研大厦") 38 | print("【精确模式】: " + "/ ".join(seg_list)) 39 | print(", ".join(seg_list)) 40 | 41 | # 新词识别 42 | 43 | 44 | # 搜索引擎模式 45 | seg_list = jieba.cut_for_search("小明硕士毕业于中国科学院计算所，后在日本京都大学深造") 46 | print(", ".join(seg_list)) 47 | ``` -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-损失函数.md: -------------------------------------------------------------------------------- 1 | 专题-损失函数 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [交叉熵](#交叉熵) 9 | - [计算示例](#计算示例) 10 | 11 | 12 | 13 | ## 交叉熵 14 | 15 | 16 | ### 计算示例 -------------------------------------------------------------------------------- /机器学习-深度学习-NLP/专题-背景知识.md: -------------------------------------------------------------------------------- 1 | 专题-基础知识 2 | === 3 | 机器学习与深度学习中的基础知识 4 | 5 | Index 6 | 7 | ## 判别模型 & 生成模型 8 | 9 |

判别模型

10 | 11 | - -------------------------------------------------------------------------------- /算法/README.md: -------------------------------------------------------------------------------- 1 | **题解** 2 | --- 3 | - [剑指Offer 题解](./题解-剑指Offer.md) 4 | - [Leetcode 题解](./题解-LeetCode.md) 5 | 6 | **专题** 7 | --- 8 | - [位运算](./专题-位运算.md) 9 | - [动态规划](./专题-动态规划.md) 10 | - [局部敏感哈希](./专题-局部敏感哈希.md) TODO 11 | - [经典算法问题](./专题-经典算法问题.md) TODO 12 | - 蓄水池抽样 13 | - 鹰蛋问题 14 | - ... 15 | 16 | **备忘** 17 | --- 18 | - [IO 模板](./备忘-IO模板.md) 19 | - [必备算法](./备忘-必备算法.md) 20 | 21 | Index 22 | --- 23 | 24 | 25 | - [Trick](#trick) 26 | - [时间复杂度](#时间复杂度) 27 | - [敏感空间](#敏感空间) 28 | - [排序](#排序) 29 | - [稳定排序与不稳定排序](#稳定排序与不稳定排序) 30 | - [堆排序-建堆的时间复杂度 `O(N)`](#堆排序-建堆的时间复杂度-on) 31 | 32 | 33 | 34 | # Trick 35 | 36 | ## 时间复杂度 37 | > [百度：ACM trick](https://www.baidu.com/s?wd=ACM%20trick) 38 | - 暴力枚举永远是第一个考虑的方法，但也有一些前提 39 | - 如果输入规模 `< 1e4`，那么可以尝试考虑 `O(n^2)` 的算法（两次循环暴力枚举）；如果 `≥ 1e5`，那么可能就需要考虑 `O(nlogn)` 的算法了——这不是绝对的，也可以通过剪枝等操作加速。 40 | - [C++] string 拼接的速度依次为：1）使用 `stringstream`；2）使用 `append()`；3）使用 `s += c`；4）使用 `s = s + c`——如果有大量拼接操作，要避免使用最后一种方法。 41 | 42 | ## 敏感空间 43 | > [ACM Trick点&&常用操作记录(持续更新)（敏感空间）](https://blog.csdn.net/feynman1999/article/details/79588347) - CSDN博客 44 | - `long long` 上界：`2^63-1 = 9,223,372,036,854,775,807 ≈ 9.223372e+18` 45 | - 阶乘：`20! = 2432902008176640000 ≈ 2.432902e+18` OK，`21!`超 46 | - Fibnacci 数列：`fib[92] = 7540113804746346429 ≈ 7.540114e+18` OK，`fib[93]` 超 47 | - Catalan 数： 48 | - 整数划分： 49 | - **数组大小**：如果内存限制为 2MB，那么最大可开辟的 int 数组为 `2*1024*1024/4 = 524288 ≈ 50,0000`，char 数组为 `2*1024*1024 = 2,097,152 ≈ 200,0000` 50 | - 实际单个数组是远开不了那么大，比如 windows 默认的栈好像只有 1MB 51 | - 在无法修改栈大小的情况下，可以使用 `new` 或 `malloc` 在**堆**上开辟内存 52 | ```C 53 | int *pa = malloc(sizeof(int)*1024*1024); 54 | int *pb = new int[1024*1024]; 55 | ``` 56 | 57 | # 排序 58 | 59 | ## 稳定排序与不稳定排序 60 | **稳定排序** 61 | - 冒泡排序、插入排序、归并排序、基数排序 62 | 63 | **不稳定排序** 64 | - 选择排序、快速排序、希尔排序、堆排序 65 | 66 | ## 堆排序-建堆的时间复杂度 `O(N)` 67 | - 堆排序的时间复杂度是 `O(NlogN)` 没有问题 68 | - 但是建堆的时间复杂度是 `O(N)` 69 | > [为什么建立一个二叉堆的时间为O(N)而不是O(Nlog(N))?](https://www.zhihu.com/question/264693363/answer/291397356) - 知乎 -------------------------------------------------------------------------------- /算法/专题-位运算.md: -------------------------------------------------------------------------------- 1 | 专题-位运算 2 | === 3 | 4 | Index 5 | --- -------------------------------------------------------------------------------- /算法/专题-大数运算.md: -------------------------------------------------------------------------------- 1 | 专题-大数运算 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [大数取模](#大数取模) 9 | - [取模运算的性质](#取模运算的性质) 10 | - [快速幂取模](#快速幂取模) 11 | - [大数加/减/乘/除](#大数加减乘除) 12 | 13 | 14 | 15 | ## 大数取模 16 | 17 | ### 取模运算的性质 18 |
19 | 20 | - 因为 `(a%n) - (b%n)` 可能小于 `n`，所以 `+n` 21 | - 因为 `(a%n)(b%n)` 可能溢出，计算前应该强转为 `long long` 22 | 23 | **Code - C++** 24 | - 输入 `a` 为长度小于 1000 的字符串，`b` 为小于 `100000` 的整数 25 | ```C++ 26 | int big_mod(const string& a, int b) { 27 | long ret = 0; // 防止 ret * 10 溢出 28 | for (auto c : a) { 29 | ret = ((ret * 10) % b + (c - '0') % b) % b; // ret = ((ret * 10) + (c - '0')) % b 30 | } 31 | return (int)ret; 32 | } 33 | 34 | /* 示例说明 35 | 1234 % 11 == ((((0*10 + 1)*10 + 2)*10 + 3)*10 + 4) % 11 36 | == ((((0*10 + 1)*10 + 2)*10 + 3)*10 % 11 + 4 % 11) % 11 37 | == ((((0*10 + 1)*10 + 2)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 38 | == ((((0*10 + 1)*10 % 11 + 2 % 11)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 39 | == ((((0*10 % 11 + 1 % 11)*10 % 11 + 2 % 11)*10 % 11 + 3 % 11)*10 % 11 + 4 % 11) % 11 40 | ``` 41 | 42 | ### 快速幂取模 43 | - 计算 `a^n % b` 44 | - **基本方法**：根据取模的性质 3 —— `ab % m == (a%m)(b%m) % m` 45 | ```C++ 46 | int big_mod(int a, int n, int b) { 47 | long long ret = 1; 48 | while(n--) { 49 | ret *= a % b; 50 | ret %= b; 51 | } 52 | return (int)ret; 53 | 54 | } 55 | ``` 56 | - 时间复杂度 `O(N)` 57 | - **快速幂取模** 58 | ```C++ 59 | int big_mod(int a, int n, int b) { 60 | long long ret = 1; 61 | while(n) { 62 | if (n & 1) 63 | ret = (ret*a) % b; 64 | a = (a*a) % b; 65 | n >>= 1; 66 | } 67 | return (int)ret; 68 | } 69 | ``` 70 | - 代码跟快速幂很像 71 | - 示例说明 72 | ``` 73 | 2^10 % 11 == (2^5 % 11)(2^5 % 11) % 11 74 | == ((2 % 11)(2^4 % 11))((2 % 11)(2^4 % 11)) % 11 75 | == ... 76 | ``` 77 | 78 | ## 大数加/减/乘/除 79 | > [大数的四则运算（加法、减法、乘法、除法） - 落枫飘飘](https://www.cnblogs.com/wuqianling/p/5387099.html) - 博客园 -------------------------------------------------------------------------------- /算法/专题-局部敏感哈希.md: -------------------------------------------------------------------------------- 1 | 局部敏感哈希 2 | === 3 | 4 | Index 5 | --- 6 | 7 | 8 | - [什么是局部敏感哈希](#什么是局部敏感哈希) 9 | - [LSH 的特点](#lsh-的特点) 10 | - [LSH 的应用场景](#lsh-的应用场景) 11 | 12 | 13 | 14 | 15 | ## 什么是局部敏感哈希 16 | > [Locality-sensitive hashing](https://en.wikipedia.org/wiki/Locality-sensitive_hashing) - Wikipedia 17 | - **局部敏感哈希**（Locality-Sensitive Hashing, LSH）指的是一类特殊的 Hash 函数——它们能使相似的高维数据在经过**降维**后依然保持一定的相似性。 18 | - LSH 本质上是一种利用 Hash 进行快速**相似度计算**的算法。 19 | - 具体来说，相似的高维数据经过 LSH 处理后，会以较高的**概率**进入相同的“**桶(bucket)**”中，从而达到分类相似数据的目的。 20 | 21 | ### LSH 的特点 22 | - **速度快**——利用 Hash 特点，LSH 特别用于处理**海量数据**的相似性计算； 23 | 24 | 25 | ### LSH 的应用场景 -------------------------------------------------------------------------------- /算法/专题-海量数据处理.md: -------------------------------------------------------------------------------- 1 | 专题-海量数据处理 2 | === 3 | 4 | Reference 5 | --- 6 | - [海量数据处理面试题集锦](https://blog.csdn.net/v_july_v/article/details/6685962) - CSDN博客 7 | - [十道海量数据处理面试题与十个方法大总结](https://blog.csdn.net/v_JULY_v/article/details/6279498) - CSDN博客 -------------------------------------------------------------------------------- /算法/专题-经典算法问题.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/算法/专题-经典算法问题.md -------------------------------------------------------------------------------- /算法/备忘-必备算法.md: -------------------------------------------------------------------------------- 1 | 备忘-必备算法 2 | === 3 | - 一些必备算法，主要是 C++ 版本 4 | Index 5 | --- 6 | 7 | 8 | - [二分查找](#二分查找) 9 | - [离散版](#离散版) 10 | - [`my_binary_search(vector, int)`](#my_binary_searchvectorint-int) 11 | - [`my_lower_bound(vector, int)`](#my_lower_boundvectorint-int) 12 | - [`my_upper_bound(vector, int)`](#my_upper_boundvectorint-int) 13 | - [排序](#排序) 14 | - [堆排序](#堆排序) 15 | - [建堆的时间复杂度](#建堆的时间复杂度) 16 | 17 | 18 | 19 | 20 | ## 二分查找 21 | 22 | ### 离散版 23 | 24 | #### `my_binary_search(vector, int)` 25 | - 没有重复元素时，目标值若存在，则返回索引；若不存在，返回 -1 26 | - 存在重复元素时，目标值若存在，则返回最小索引；若不存在，返回 -1 27 | ```C++ 28 | int my_binary_search(vector& nums, int v) { 29 | if (nums.size() < 1) return - 1; 30 | 31 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 32 | 33 | while (hi - lo > 1) { 34 | int mid = lo + (hi - lo) / 2; 35 | if (nums[mid] < v) 36 | lo = mid; 37 | else 38 | hi = mid; 39 | } 40 | 41 | return nums[lo + 1] == v ? lo + 1 : -1; 42 | } 43 | ``` 44 | 45 | #### `my_lower_bound(vector, int)` 46 | - 返回大于、等于目标值的最小索引（第一个大于或等于目标值的索引） 47 | ```C++ 48 | int my_lower_bound(vector& nums, int v) { 49 | if (nums.size() < 1) return -1; 50 | 51 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 52 | 53 | while (hi - lo > 1) { // 退出循环时有：lo + 1 == hi 54 | int mid = lo + (hi - lo) / 2; 55 | if (nums[mid] < v) 56 | lo = mid; // 因为始终将 lo 端当做开区间，所以没有必要 `lo = mid + 1;` 57 | else 58 | hi = mid; // 而在 else 中，mid 可能就是最后的结果，所以不能 `hi = mid - 1` 59 | } 60 | 61 | return lo + 1; // 相比 binary_search，只有返回值不同 62 | } 63 | ``` 64 | - **为什么返回 `lo + 1`？** 65 | - 模板开始时将 (lo, hi) 看做是一个开区间，通过不断二分，最终这个区间中只会含有一个值，即 (lo, hi] 66 | - 返回 lo+1 的含义是，结果就在 lo 的下一个； 67 | - 在迭代的过程中，hi 会从开区间变为闭区间，而 lo 始终是开区间，返回 lo+1 显得更加统一。 68 | - 当然，这跟迭代的写法是相关的，你也可以使最终的结果区间是 [lo, hi)，这取决于个人习惯。 69 | 70 | #### `my_upper_bound(vector, int)` 71 | - 返回大于目标值的最小索引（第一个大于目标值的索引） 72 | ```C++ 73 | int my_upper_bound(vector& nums, int v) { 74 | if (nums.size() < 1) return -1; 75 | 76 | int lo = -1, hi = nums.size(); // hi = nums.size() - 1 77 | 78 | while (hi - lo > 1) { 79 | int mid = lo + (hi - lo) / 2; 80 | 81 | if (nums[mid] <= v) // 相比 lower_bound，唯一不同点：`<` -> `<=` 82 | lo = mid; 83 | else 84 | hi = mid; 85 | } 86 | 87 | return lo + 1; 88 | } 89 | ``` 90 | 91 | ## 排序 92 | 93 | ### 堆排序 94 | ```C++ 95 | 96 | ``` 97 | 98 | #### 建堆的时间复杂度 99 | > [为什么建立一个二叉堆的时间为O(N)而不是O(Nlog(N))?](https://www.zhihu.com/question/264693363/answer/291397356) - 知乎 100 | -------------------------------------------------------------------------------- /算法/题解-LeetCode.md: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/worksking/Interview_Notes-Chinese/1fbf6370d35d33b57b9feb9ef9fb8f7a01269e10/算法/题解-LeetCode.md -------------------------------------------------------------------------------- /编程语言/Cpp-面向对象编程.md: -------------------------------------------------------------------------------- 1 | C++-面向对象编程 2 | === 3 | 4 | 面向对象编程概述 5 | --- 6 | - 面向对象编程的两个主要特征：**继承**、**多态** 7 | - C++ 中是通过间接利用“**指向父类**”的**指针**或**引用**来操作其子类对象，从而达到多态的目的；如果直接操作某个实例，那么多态将无从谈起 8 | - 具体来说，**多态**是通过**动态绑定**机制，达到在**运行时**确定实际被调用的是哪个子类类型，进而调用对应的 **override** 方法 9 | 10 | Reference 11 | --- 12 | - 《Essential C++》 第 4/5 章 - Lippman, 侯捷 13 | 14 | Index 15 | --- 16 | 17 | 18 | - [不通过继承实现多态](#不通过继承实现多态) 19 | - [抽象基类](#抽象基类) 20 | 21 | 22 | 23 | ## 不通过继承实现多态 24 | > 4.11 指针，指向 Class member function 25 | - 核心想法：手工调整函数指针，模拟**动态绑定**的效果 26 |
示例：动态序列（点击展开） 27 | 28 | ```Cpp 29 | class num_sequence { 30 | public: 31 | // PtrType 是一个指针，指向 num_sequence 的成员函数， 32 | // 该成员函数必须只接受一个 int 型参数，以及返回类型为 void 33 | typedef void (num_sequence::*PtrType)(int); 34 | 35 | enum { cnt_seq = 2 }; // 预定义了两种序列 36 | enum ns_type { 37 | ns_fibonacci, ns_square 38 | }; 39 | 40 | // 构造函数：默认指向斐波那契数列 41 | num_sequence(): _pmf(func_tbl[ns_fibonacci]) { } 42 | 43 | // 调整指针指向 44 | void set_sequence(ns_type nst) { 45 | switch (nst) { 46 | case ns_fibonacci: case ns_square: 47 | _pmf = func_tbl[nst]; 48 | break; 49 | default: 50 | cerr << "invalid sequence type\n"; 51 | } 52 | } 53 | 54 | void print(int n) { 55 | (this->*_pmf)(n); // 通过指针选择需要调用的函数 56 | } 57 | 58 | // _pmf 可以指向以下任何一个函数 59 | void fibonacci(int n) { 60 | int f = 1; 61 | int g = 1; 62 | for (int i = 2; i <= n; i++) 63 | g = g + f, f = g - f; 64 | cout << f << endl; 65 | } 66 | 67 | void square(int n) { 68 | cout << n * n << endl; 69 | } 70 | 71 | private: 72 | PtrType _pmf; 73 | static PtrType func_tbl[cnt_seq]; // 保存所有序列函数的指针 74 | // 为了兼容性，不推荐写成 `static vector没有空格> _seq;` 75 | }; 76 | 77 | // static 成员变量初始化 78 | num_sequence::PtrType 79 | num_sequence::func_tbl[cnt_seq] = { 80 | &num_sequence::fibonacci, 81 | &num_sequence::square, 82 | }; 83 | 84 | int main() { 85 | 86 | auto ns = num_sequence(); 87 | ns.print(5); // 5 88 | ns.set_sequence(num_sequence::ns_square); // 调整函数指针以获得多态的效果 89 | ns.print(5); // 25 90 | 91 | cout << endl; 92 | system("PAUSE"); 93 | return 0; 94 | } 95 | ``` 96 | 97 |
98 | 99 | > [源文件](../code/cpp/面向对象-不通过继承实现多态-动态序列.cpp) 100 | 101 | ## 抽象基类 102 | - 仅仅为了**设计**/**定义规范**而存在 -------------------------------------------------------------------------------- /编程语言/README.md: -------------------------------------------------------------------------------- 1 | 编程语言相关知识点总结 2 | === 3 | - 以 C++ 和 Python 为主 4 | 5 | **Index** 6 | --- 7 | - [C/C++ 语言知识点](./CCpp编程语言.md) 8 | - [专题-左值与右值](./Cpp专题-左值与右值) 9 | 10 | ## Reference 11 | - - huihut/[interview: C/C++面试知识总结](https://github.com/huihut/interview) 12 | 13 | ## ToDo 14 | - Cpp专题-STL 15 | - Cpp专题-面向对象 --------------------------------------------------------------------------------