人工智能方向名企NLP第4期，视频+资料百度云 价值10800元(更新第7期)

01-核心能力提升班自然语言处理方向004期

1.1 语言模型与语法树

1-语言模型与语法树.mp4

assignment-01-optional-pattern-match.ipynb

Assignment-01.ipynb

Lecture-1.pptx

lesson_1class.ipynb

sqlResult_1558435.zip

优秀作业推荐-核心lesson01-吴20200829.rar

数据集二.txt

作业数据集

movie_comments.csv

train.txt.gz

10.1 CNN卷积神经网络

10-CNN卷积神经网络.mp4

lecture-10.pdf

homework

1_LeNet.ipynb

2_AlexNet.ipynb

3_Pairwise_test.ipynb

11.1 RNN循环神经网络

11-RNN循环神经网络.mp4

lecture-11.pdf

homework11

11_lesson.ipynb

12.1 Transformer与BERT，大规模预训练问题

12-Transformer与BERT，大规模预训练问题.mp4

lecture-12.pdf

13.1 面向服务的智能客户机器人与新闻自动摘要生成

13-面向服务的智能客户机器人与新闻自动摘要生成.mp4

lecture-13.pptx

seq2seq_chatbot.ipynb

14.1 高级人工智能知识

14-高级人工智能知识.mp4

lecture-14.pptx

Assignment14

Assignment14.ipynb

debug.log

result.txt

tang.npz

.ipynb_checkpoints

Assignment14-checkpoint.ipynb

result-checkpoint.txt

checkpoints

2.1 爬虫、搜索引擎与 自动路径决策

2-爬虫、搜索引擎与自动路径决策.mp4

Assignment_02.ipynb

Lecture_02.ipynb

Lecture_2 copy.pptx

Lecture_2.pptx

优秀作业.docx

3.1 动态规划与编辑距离

3-动态规划与编辑距离.mp4

article_9k.txt

Assignment_3.ipynb

Lecture_3.ipynb

Lecture_3_new.pptx

核心课lesson03优秀作业-吴.rar

4.1 自然语言理解初步

4-自然语言理解初步.mp4

Assignment_4.ipynb

Lecture_3.ipynb

核心Lesson04优秀作业_wu20200923.rar

第四课 copy.pptx

第四课.pptx

5.1 经典机器学习一

5-经典机器学习一.mp4

lecture5-zhang(1).pdf

核心优秀作业5-.zip

核心优秀作业5-.zip

6.1 深度学习

6-深度学习.mp4

lecture6.pptx

7.1 经典机器学习二

7-经典机器学习二.mp4

lecture7.pdf

神经网络工具代码的github地址.docx

8.1 经典机器学习三：非监督、 半监督、主动学习

8-经典机器学习三：非监督、半监督、主动学习.mp4

lecture-8-课后.pptx

lecture-8.pptx

9.1 word2vec

9-word2vec.mp4

lecture-9(1).pptx

lecture-9(2).pptx

lesson9

homework.ipynb

02-导师制名企实训班自然语言处理方向004期-项目一

1.1 项目导论与中文 词向量实践

名企lesson01优秀作业推荐-吴.rar

导师班4期lecture-week1.pdf

导师班4期week1课后有笔记.pdf

课上代码Lecture1(1).ipynb

项目导论与中文词向量实践.mp4

week1-homework

.DS_Store

homework-week1要求.md

utils

build_w2v.py

dataset_split.py

data_reader.py

data_utils.py

preprocess.py

tokenizer.py

__init__.py

2.1 基于Seq2Seq架构的模型搭建

2.1 基于Seq2Seq架构的模型搭建.mp4

Lecture2(1).ipynb

导师4期lecture-week2-课前.pdf

导师4期lecture-week2-课后有笔记.pdf

朱毅优秀作业1.zip

李玉俊.zip

王浩然.zip

homework-week2

.DS_Store

homework-week2要求.md

seq2seq_tf2

.DS_Store

batcher.py

train_eval_test.py

train_helper.py

__init__.py

bin

main.py

__init__.py

decoders

rnn_decoder.py

__init__.py

encoders

rnn_encoder.py

__init__.py

models

sequence_to_sequence.py

__init__.py

3.1 NLG过程的优化与项目Inference

3.1 NLG过程的优化与项目Inference.mp4

lecture-3(1).ipynb

名企Lesson03优秀作业_wu20200923.rar

导师班4期week3课前.pdf

导师班4期week3课后有笔记.pdf

homework-3

.DS_Store

homework-week3要求.md

seq2seq_tf2

.DS_Store

batcher.py

eval.py

test_helper.py

train_eval_test.py

train_helper.py

__init__.py

bin

main.py

__init__.py

decoders

.DS_Store

rnn_decoder.py

__init__.py

encoders

.DS_Store

rnn_encoder.py

__init__.py

models

.DS_Store

sequence_to_sequence.py

__init__.py

utils

.DS_Store

build_w2v.py

dataset_split.py

data_reader.py

data_utils.py

io_utils.py

log_utils.py

new.py

preprocess.py

tokenizer.py

__init__.py

4.1 OOV和Word-repetition问题的改进

4.1 OOV和Word-repetition问题的改进.mp4

lecture-4(1).ipynb

名企Lesson04优秀作业_wu20200923.rar

导师班4期week4课前.pdf

导师班4期week4课后有笔记.pdf

homework-4

.DS_Store

homework-week4要求.md

seq2seq_pgn_tf2

.DS_Store

batcher.py

eval.py

test_helper.py

train_eval_test.py

train_helper.py

__init__.py

bin

main.py

__init__.py

decoders

.DS_Store

rnn_decoder.py

__init__.py

encoders

.DS_Store

rnn_encoder.py

__init__.py

models

.DS_Store

pgn.py

__init__.py

utils

decoding.py

losses.py

__pycache__

decoding.cpython-36.pyc

decoding.cpython-37.pyc

losses.cpython-36.pyc

losses.cpython-37.pyc

utils

.DS_Store

build_w2v.py

dataset_split.py

data_reader.py

data_utils.py

io_utils.py

log_utils.py

new.py

preprocess.py

tokenizer.py

__init__.py

5.1 基于Transformer特征提取器的改进

5.1 基于Transformer特征提取器的改进.mp4

homework-week5(1).md

lecture5课上代码.ipynb

名企Lesson05优秀作业_wu20201009.rar

导师班4期week5课前.pdf

导师班4期week5课后有笔记.pdf

6.1 BERT在抽取式任务中的效果

6.1 BERT在抽取式任务中的效果.mp4

homework-week6(1).md

sentiment_bert_pytorch(1).ipynb

名企Lesson06优秀作业_wu20201013.rar

导师4期lecture-week6课前.pdf

导师4期lecture-week6课后有笔记.pdf

7.1 预训练模型在摘要任务中的改进

7.1 预训练模型在摘要任务中的改进.mp4

homework-week7(1).md

优秀作业.rar

导师班4期week7课前.pdf

导师班4期week7课后有笔记.pdf

8.1 项目总结与回顾

8.1 项目总结与回顾.mp4

homework-week8(1).md

名企Lesson08优秀作业.rar

导师班4期week8课前.pdf

导师班4期week8课后有笔记.pdf

03-基于大规模预训练模型的机器阅读理解-项目二

1.1 机器阅读理解发展及任务解析

1-机器阅读理解发展及任务解析.mp4

dureader.txt

homework1.ipynb

homework_01_说明.txt

ingetrated_stopwords.txt

lesson_01_mark.pptx

squad.txt

datas

dev-v1.1.json

dev-v2.0.json

dureader_robust-data.tar.gz

dureader_robust-test1.tar.gz

dureader_robust-test2.tar.gz

sampledata.tar.gz

train-v1.1.json

train-v2.0.json

tfidf_practice

README.md

tffidf_practice.py

datas

input

dureader_robust

test1.json

train.json

SQuAD

dev-v1.1.json

dev-v2.0.json

train-v1.1.json

train-v2.0.json

output

dureader_robust

dr_tf.json

dr_tfidf.json

SQuAD

SQuAD_tf.json

SQuAD_tfidf.json

nlp_source

baidu_stopwords.txt

2.1 常见机器阅读理解模型（一）

2-常见机器阅读理解模型（一）.mp4

homework_02_说明.txt

lesson_02_mark.pptx

main.py

preprocess.py

homework_02_code

homework_02_code

BiDAF_tf2

data_io.py

main.py

preprocess.py

data

squad

dev-v1.1.json

train-v1.1.json

layers

attention.py

highway.py

merge.py

similarity.py

span.py

__init__.py

__pycache__

attention.cpython-36.pyc

attention.cpython-37.pyc

highway.cpython-36.pyc

highway.cpython-37.pyc

merge.cpython-36.pyc

merge.cpython-37.pyc

similarity.cpython-36.pyc

similarity.cpython-37.pyc

span.cpython-36.pyc

span.cpython-37.pyc

__init__.cpython-36.pyc

__init__.cpython-37.pyc

__pycache__

data_io.cpython-36.pyc

data_io.cpython-37.pyc

preprocess.cpython-36.pyc

preprocess.cpython-37.pyc

3.1 常见机器阅读理解模型（二）

3-常见机器阅读理解模型（二）.mp4

BiDAF_pytorch参考代码.rar

BiDAF_tf2作业说明+第二节参考答案(1).rar

BiDAF_torch.ipynb

data_io.py

homework_03_说明.txt

lesson_03_mark.pptx

main.py

preprocess.py

运行截图.png

运行截图2.png

阅读理解项目Lesson03优秀作业.rar

layers

attention.py

highway.py

merge.py

similarity.py

span.py

__init__.py

__pycache__

attention.cpython-37.pyc

highway.cpython-37.pyc

merge.cpython-37.pyc

similarity.cpython-37.pyc

span.cpython-37.pyc

__init__.cpython-37.pyc

4.1 BERT与机器阅读理解

4-BERT与机器阅读理解.mp4

BERT-Config.png

bert-start.png

data_io.py

extract_features.py

homework_04_说明.txt

lesson_04.pptx

lesson_04_codes.rar

main.py

practice.py

preprocess.py

README.md

word_embedding_1.png

word_embedding_2.png

word_embedding_3.png

word_embedding_4.png

word_embedding_5.png

阅读理解项目lesson04优秀作业.rar

layers

attention.py

attention_teacher.py

highway.py

merge.py

similarity.py

span.py

__init__.py

__pycache__

attention.cpython-36.pyc

attention.cpython-37.pyc

highway.cpython-36.pyc

highway.cpython-37.pyc

merge.cpython-36.pyc

merge.cpython-37.pyc

similarity.cpython-36.pyc

similarity.cpython-37.pyc

span.cpython-36.pyc

span.cpython-37.pyc

__init__.cpython-36.pyc

__init__.cpython-37.pyc

5.1 BERT的模型变体

5-BERT的模型变体.mp4

homework_05.txt

lesson_05.pptx

modeling.py

XLNET分析.docx

阅读理解项目lesson05优秀作业.rar

6.1 其它阅读理解相关模型

6-其它阅读理解相关模型.mp4

6作业讲解.pdf

lesson_06.pptx

lesson_06_codes.rar

lesson_06_homework.txt

简答题.docx

罗建军_机器阅读理解week6作业.docx

7.1 模型集成与部署

7-模型集成与部署.mp4

7作业讲解.pdf

ensemble.py

lesson_07.pptx

lesson_07_code.rar

lesson_07_homework.txt

week7_笔记_田子敬.docx

第七章笔记-黄苛.docx

mq_week7_杜宇鹏

ensemble.py

8.1 项目总结

6作业讲解.pdf

7作业讲解.pdf

8-项目总结.mp4

homework_lesson_08.txt

lesson_08_mark.pptx

名企班-week8-潘维维(学习笔记）.pdf

名企班-司德谭-week8.txt

截屏2020-12-27下午5.35.10.png

截屏2020-12-27下午9.08.51.png

机器阅读理解总结_Lesson8_王皓.pdf

homework08

dureader robust训练过程1.png

dureader robust训练过程2.png

homework08简答题.docx

风老师项目总结资料

requirements.txt

作业报错解决.txt

简历里项目怎么写.txt

阅读理解应用流程.pptx

04-企业级任务型对话机器人-项目三

1.1 智能对话系统导论

1-Conversational-AI-Overview(1).pdf

1-智能对话系统导论.mp4

Course 1 - HW - Black.pdf

week 1 作业.docx

10.1 端到端的对话系统和智能对话系统在工业中

10-端到端的对话系统和智能对话系统在工业中.mp4

8-Conversational-AI-in-Industry-and-Interview.pdf

AI面试内参.pdf

张佶-多语言阿里小蜜.pdf

paper

.DS_Store

02_pd_env_17oct2018_module_1_tool_for_change_181016cs.pdf

Multi-lingual Intent Detection and Slot Filling in a Joint BERT-based Model.pdf

MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling.pdf

Neural Approaches to Conversational AI.pdf

SCALABLE MULTI-DOMAIN DIALOGUE STATE TRACKING.pdf

Slot-Gated Modeling for Joint Slot Filling and Intent Prediction.pdf

Stylistic Control for Neural Natural Language Generation.pdf

Template Guided Text Generation for Task-Oriented Dialogue.pdf

The Design and Implementation of XiaoIce-an Empathetic Social Chatbot.pdf

TRADE.pdf

[0]Recent-Advances-and-Challenges-in-Task-oriented-Dialog-Systems.pdf

[10] The dialog state tracking challenge.pdf

[11] Learning end-to-end goal-oriented dialog..pdf

[12] Building a conversational agent overnight with dialogue self-play.pdf

[14] Convolutional neural network based triangular crf for joint intent detection and slot filling..pdf

[15] A bi-model based rnn semantic frame parsing model for intent detection and slot filling..pdf

[16] The second dialog state tracking challenge. .pdf

[17] Towards an automatic turing test Learning to evaluate dialogue responses.pdf

[18] Ruber- An unsupervised method for automatic evaluation of open-domain dialog systems.pdf

[19] MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling.pdf

[1] Docchat- An information retrieval approach for chatbot engines using unstructured documents.pdf

[20] Deep reinforcement learning for dialogue generation.pdf

[21] Partially observable Markov decision processes for spoken dialog systems.pdf

[22] Coqa- A conversational question answering challenge.pdf

[23] Learning discourse-level diversity for neural dialog models using conditional variational autoencoders.pdf

[24] Mojitalk Generating emotional responses at scale.pdf

[25] Assigning Personality_Profile to a Chatting Machine for Coherent Conversation Generation..pdf

[26] Personalizing Dialogue Agents I have a dog, do you have pets too.pdf

[27] Commonsense Knowledge Aware Conversation Generation with Graph Attention..pdf

[28] Global-to-local Memory Pointer Networks for Task-Oriented Dialogue..pdf

[2] An Information Retrieval Approach to Short Text Conversation.pdf

[31] BBQ-networks- Efficient exploration in deep reinforcement learning for task-oriented dialogue systems.pdf

[32] A network-based end-to-end trainable task-oriented dialogue system..pdf

[33] Sequicity- Simplifying task-oriented dialogue systems with single sequence-to-sequence architectures.pdf

[34] Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement Learning.pdf

[35] Deep dyna-q Integrating planning for task-completion dialogue policy learning.pdf

[36] Towards end-to-end reinforcement learning of dialogue agents for information access..pdf

[37] Curate and Generate A Corpus and Method for Joint Control of Semantics and Style in Neural NLG.pdf

[38] Few-Shot Text Classification with Induction Network.pdf

[39] Diverse few-shot text classification with multiple metrics..pdf

[3] An Ensemble of Retrieval-Based and Generation-Based Human-Computer Conversation Systems..pdf

[40] Deep recurrent q-learning for partially observable mdps.pdf

[41]A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset.pdf

[42]A SIMPLE BUT EFFECTIVE BERT MODEL FOR DIALOG STATE TRACKING ON RESOURCE-LIMITED SYSTEMS.pdf

[43]A User Simulator for Task-Completion Dialogues.pdf

[44]Attention-Based Recurrent Neural Network Models for Joint Intent Detection and Slot Filling.pdf

[45]BERT for Joint Intent Classification and Slot Filling.pdf

[46]BERT-DST- Scalable End-to-End Dialogue State Tracking with Bidirectional Encoder Representations from Transformer.pdf

[47]CrossWOZ.pdf

[48]Dialog State Tracking.pdf

[49]dst-overview.pdf

[4] Exemplar encoder-decoder for neural conversation generation.pdf

[50]How to Build User Simulators to Train RL-based Dialog Systems.pdf

[5]  chat- A sequence to sequence and rerank based chatbot engine[C]__Proceedings of the 55th Ann.pdf

[7] Multi-view response selection for human-computer conversation.pdf

[8] Multi-turn response selection for chatbots with deep attention matching network.pdf

2.1 使用 RASA 制作你的第一个对话机器人

2-RASA-ChatBot-Framework (1).pdf

2-RASA-ChatBot-Framework(1).pdf

2-使用RASA制作你的第一个对话机器人.mp4

week 2 (1).docx

week 2.docx

名企班-week2-陈国旗–rasa总结.pdf

对话项目lesson02优秀作业.rar

3.1 深入 RASA 源码和定制化你的对话机器人

3-Dive-Into-RASA-and-Customerize-Your-ChatBot (1).pdf

3-Dive-Into-RASA-and-Customerize-Your-ChatBot.pdf

3-深入RASA源码和定制化你的对话机器人.mp4

截屏2021-01-08 上午11.28.27.png

4.1 代码课-基于 rasa 做 KBQA

4-代码课-基于rasa做KBQA.mp4

cdss

actions.py

chatito.md

config.yml

credentials.yml

domain.yml

endpoints.yml

rasa常用命令.md

readme.docx

__init__.py

data

nlu.md

stories.md

jieba_userdict

Departments_dic.txt

Diseases_dic.txt

Drugs_dic.txt

Foods_dic.txt

Symptoms_dic.txt

medical

lookup

Departments.txt

Diseases.txt

Drugs.txt

Foods.txt

Symptoms.txt

graph_database

models

tests

process_data

create_graph.py

drugs.csv

medical.json

producers.csv

rels_drug_producer.csv

__init__.py

5.1 自然语言理解（NLU）

4-Natural-Language-Understanding(1).pdf

5-自然语言理解（NLU）.mp4

6.1 HuggingFace’s Transformer和基于规则的对话状态跟踪

5-Further NLU and Dialogue State Tracking.pdf

6-HuggingFacesTransformer和基于规则的对话状态跟踪.mp4

7.1 基于模型的对话跟踪和基于规则的Dialogue Policy

6-Model-based-DST-and-Rule-based-Dialogue-Policy.pdf

7-基于模型的对话跟踪和基于规则的DialoguePolicy.mp4

B34A7FD9-38E6-4B45-B517-FFABD2D15F6A.png

8.1 代码课-NLU 和 DST 联合建模方法

8-代码课-NLU和DST联合建模方法.mp4

9.1 基于模版的对话生成和有限状态机（FSM）

7-Model-based-DP-and-Template-based-NLG.pdf

9-基于模版的对话生成和有限状态机（FSM）.mp4

微信图片_20210314081233.jpg

05-数据分析与Python程序设计基础

1.1 Python 数据智能编程基础

1.1 Python数据智能编程基础.mp4

lesson01DAV0.6.pptx

Week01-BI.pdf

Week01-CV.pdf

Week01-NLP.pdf

2.1 Python 格式化数据处理 - Pandas

2.1 Python格式化数据处理-Pandas.mp4

lesson02DAV1.0.pptx

Week02-BI.pdf

Week02-CV.pdf

Week02-NLP.pdf

3.1 数据可视化

lesson03DAV0.8.pptx

Week03-BI.pdf

Week03-CV.pdf

Week03-NLP.pdf

4.1 网络信息分析

4.1 网络信息分析.mp4

assignment04-1.py

assignment04-2.py

lesson04DAV0.7.pptx

Week04-BI.pdf

Week04-CV.pdf

Week04-NLP.pdf

5.1 文本信息自动化处理

5.1 文本信息自动化处理.mp4

assignment05.py

lesson05DAV1.0.pptx

Week 05.pdf

code

emails

Emails.csv

email_lda.py

email_re_clean.py

hotel_recommendation

hotel_rec.py

Seattle_Hotels.csv

jieba

jieba1.py

news

re

re1.py

re2.py

re_is_email.py

re_is_phone.py

textrank

news.txt

news_textrank.py

news_textrank_snownlp.py

sentence_textrank.py

tfidf

tfidf

lda_gensim.py

tfidf_gensim.py

tfidf_sklearn.py

6.1 Python 办公自动化

6 Python办公自动化.mp4

assignment06_auto_email.py

assignment06_daily_report.py

lesson06DAV0.9.pdf

Week 06.pdf

code

email

email1.py

excel

xlrd1.py

xlwt1.py

日报.xls

楼宇安防.xls

楼宇安防2.xls

pdf

2018高新认证名单.csv

2018高新认证名单.pdf

book.pdf

book.pptx

book_pdf_reading.py

book_ppt_writing.py

course.html

course.pdf

course_page0.pdf

course_page1.pdf

course_rotate.pdf

course_watermark.pdf

demo.pptx

merged.pdf

pdf2htmlEX.exe

pdfminer_reading.py

pdfplumber_table.py

pypdf2_merge.py

pypdf2_read.py

pypdf2_rotate.py

pypdf2_split.py

The Youtube video recommendation system.pdf

data

base.css

base.css.in

base.min.css

build_css.sh

build_js.sh

compatibility.min.js

fancy.css

fancy.css.in

fancy.min.css

LICENSE

manifest

pdf2htmlEX-64×64.png

pdf2htmlEX.js

pdf2htmlEX.js.in

pdf2htmlEX.min.js

images

10_FXX1.jpeg

11_FXX1.jpeg

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text

1.txt

10.txt

11.txt

12.txt

13.txt

14.txt

15.txt

16.txt

17.txt

18.txt

19.txt

2.txt

20.txt

21.txt

22.txt

23.txt

24.txt

25.txt

26.txt

27.txt

28.txt

29.txt

3.txt

30.txt

31.txt

32.txt

33.txt

34.txt

35.txt

36.txt

37.txt

38.txt

39.txt

4.txt

40.txt

41.txt

42.txt

43.txt

44.txt

45.txt

46.txt

47.txt

48.txt

49.txt

5.txt

50.txt

6.txt

7.txt

8.txt

9.txt

ppt

demo.pptx

pic1.jpg

ppt1.py

pptx_usage.pptx

ppt_action.py

word

pic1.jpg

test.docx

word1.py

7.1 服务器、数据库与分布式系统

7 Python办公自动化.mp4

assignment07.py

lesson07DAV0.5.pdf

Week 07.pdf

code

heros.sql

hadoop_code

input.txt

mapper.py

reducer.py

hotel_recommendation

hotel_rec.py

pyspark_tfidf.py

Seattle_Hotels.csv

kpl_data

hero_temp.py

kpl_data.py

pyspark

heros.csv

input.txt

pyspark_dataframe.py

pyspark_wordcount.py

06-微软九步AI学习法-人工智能核心知识强化课程

1.1 搜索树，图算法，深度优化与广度优化，算法的时间复杂度

Assignment01.ipynb

Git 与版本控制、代码风格.pptx

Git 思维导图.xmind

Git与版本控制、代码风格.mp4

networkx如何设置中文.ipynb

SimHei.ttf

image-retrieval-master

data.json

demo.py

feature_extractor.py

LICENSE

offline.py

README.md

requirements.txt

server.py

.idea

misc.xml

modules.xml

sis-master.iml

vcs.xml

workspace.xml

inspectionProfiles

profiles_settings.xml

static

feature

img

uploaded

templates

index.html

__pycache__

feature_extractor.cpython-36.pyc

lesson01-course

bfs.jpg

bfs_2.jpg

Computing Complexity.jpg

c_c.jpg

dfs.jpg

dfs_2.jpg

diff_cc.jpg

edit_distance.jpg

edit_distance_math.jpg

edit_distance_t.jpg

graph.jpg

graph_type.jpg

houchang.jpg

lesson_1.ipynb

queue.jpg

random_al.jpg

stack.jpg

tree.jpg

.ipynb_checkpoints

Computing Complexity-checkpoint.jpg

dfs-checkpoint.jpg

edit_distance_math-checkpoint.jpg

lesson_1-checkpoint.ipynb

图像检索项目指导书与数据

.DS_Store

dataset.zip

glove.6B.zip

ukbench.zip

图像检索项目指导书.pptx

1.2 第一周作业讲解

Assignment_01.ipynb

第一周作业讲解.mp4

2.1 神经网络基础，tensorflow和pytorch框架

Assignment02.pdf

Assignment02.py

Assignment02_numpy_boston.py

lesson02AIV1.4.pptx

神经网络基础，tensorflow和pytorch框架.mp4

code

is_similarity.py

make_moons_pytorch.py

numpy_model.py

activation_function

relu1.py

sigmod1.py

tanh1.py

boston_price

house_price

house_price

linear_prediction1.py

linear_prediction2.py

3.1 深度卷积网络与计算机图像1

ai-core-lesson-03-cnn V1.1.pdf

基于CNN模型的物体识别.ipynb

深度卷积网络与计算机图像1.mp4

微软_lesson03

alexnet.png

BackBone.jpg

bn_algorithm.png

bottleneck.jpg

classification.jpg

conv2d_padding.gif

densenet.jpg

dropout.jpg

feature_maps.png

googlenet.png

inception.jpg

lesson_3.ipynb

Local_invariant.png

pool.png

pooling.jpeg

receptive_field.png

ResNet_structure.jpg

same_padding_no_strides_transposed.gif

stride.jpeg

strides.png

valid_padding.jpeg

VGG16.png

vgg_FLOPs.jpg

故宫.jpg

.ipynb_checkpoints

lesson_3-checkpoint.ipynb

基于CNN模型的物体识别-checkpoint.ipynb

3.2 深度卷积网络与计算机图像2

Assignment 03_2.pdf

Assignment03-refer.py

cnn_feature_map_demo.py

lesson03AIV1.3.pdf

深度卷积网络与计算机图像2.mp4

code

alexnet1.py

Assignment03-refer.py

cifar10_resnet.py

cnn_feature_map_demo.py

cnn_viz.py

dog.jpg

mnist_alexnet.py

stanford_car_dataset.py

stanford_car_resnet.py

4.1 循环神经网络，文本表征，词向量初步，文本自动分类

Assignment 04.pdf

lesson04AIV1.7.pdf

循环神经网络，文本表征，词向量初步，文本自动分类.mp4

搜索树，图算法，深度优化与广度优化，算法的时间复杂度.mp4

cnews

cnews_loader.py

model.py

test.py

train.py

train_sampling.py

code

cnews

cnews.test.txt

cnews.train.txt

cnews.val.txt

cnews.vocab.txt

cnews_loader.py

data_batch.py

model.py

test.py

train_sampling.py

flights

flights.csv

flight_linear.py

flight_rnn.py

mnist

mnist_rnn.py

5.1 Seq2Sequence，机器自动翻译， Image Caption, Attention机制

Assignment_05.ipynb

cmn.txt

Lesson_05.pdf

lesson_05_code.ipynb

Seq2Sequence，机器自动翻译，ImageCaption,Attention机制.mp4

_about.txt

6.1 贝叶斯，决策树，随机森林，SVM模型

assignment06作业参考答案.py

lesson06AIV0.4.pdf

lesson06AIV0.8.pptx

lesson06AIV0.8（PDF）.pdf

贝叶斯，决策树，随机森林，SVM模型.mp4

Assignment06

chinese_stopwords.txt

sqlResult.csv

L6

mnist_clf.py

mnist_semi.py

NORMDIST.py

Assignment06

chinese_stopwords.txt

sqlResult.csv

team_cluster

team_cluster_data.csv

team_cluster_hierarchy.py

team_cluster_kmeans.py

7.1 加课：seq2seq的代码及作业的讲解

bleu1.py

lesson05AIV1.0.pptx

lesson05AIV1.2.pptx

加课：seq2seq的代码及作业的讲解.mp4

07-0基础 Python 入门

1.1 Python 基础入门

go.py

python-1-Python基础入门.mp4

week1-python入门基础.ipynb

2.1 Python 编程入门

python-2-Python编程入门.mp4

week2-python编程基础

data_structure.jpg

division.py

fight.jpg

houchang_night.jpg

landuo.jpg

list_index.jpg

sanguosha.jpg

trust.jpg

weapon.jpg

weapon.py

week2-python编程基础.ipynb

实现一个栈_题目解析.ipynb

实现一个队列-题目解析.ipynb

.ipynb_checkpoints

division-checkpoint.py

weapon-checkpoint.py

week2-python编程基础-checkpoint.ipynb

实现一个栈_题目解析-checkpoint.ipynb

实现一个队列-题目解析-checkpoint.ipynb

__pycache__

division.cpython-37.pyc

weapon.cpython-37.pyc

3.1 常用模块-numpy

python-3-常用模块-numpy.mp4

week3-numpy

arr_index.jpg

data_s_1.jpg

data_s_2.jpg

guangbo.jpg

np_type1.jpg

np_type2.jpg

python3爬虫—百度百科（疾病信息）.md

random_1.jpg

random_2.jpg

ufun_1d_1.jpg

ufun_1d_2.jpg

ufun_2d_1.jpg

week3-numpy.ipynb

正则表达式.ipynb

爬取空气质量-理解爬虫原理.ipynb

.ipynb_checkpoints

python3爬虫—百度百科（疾病信息）-checkpoint.md

week3-numpy-checkpoint.ipynb

正则表达式-checkpoint.ipynb

爬取空气质量-理解爬虫原理-checkpoint.ipynb

4.1 常用模块-pandas

python-4-常用模块-pandas.mp4

week4-pandsa.zip

week4-pandsa

index_m.jpg

math.jpg

pandas_getdata.jpg

pandas_stat.jpg

rank_m.jpg

reindex_f.jpg

week4-pandas初步.ipynb

.ipynb_checkpoints

week4-pandas初步-checkpoint.ipynb

5.1 数据可视化

python-5-数据可视化.mp4

week5-数据可视化.ipynb

6.1 Python 办公自动化

lesson06DAV0.9.pptx

python-6-Python办公自动化.mp4

code

email

email1.py

excel

xlrd1.py

xlwt1.py

日报.xls

楼宇安防.xls

楼宇安防2.xls

pdf

2018高新认证名单.csv

2018高新认证名单.pdf

book.pdf

book.pptx

book_pdf_reading.py

book_ppt_writing.py

course.html

course.pdf

course_page0.pdf

course_page1.pdf

course_rotate.pdf

course_watermark.pdf

demo.pptx

merged.pdf

pdf2htmlEX.exe

pdfminer_reading.py

pdfplumber_table.py

pypdf2_merge.py

pypdf2_read.py

pypdf2_rotate.py

pypdf2_split.py

The Youtube video recommendation system.pdf

data

base.css

base.css.in

base.min.css

build_css.sh

build_js.sh

compatibility.min.js

fancy.css

fancy.css.in

fancy.min.css

LICENSE

manifest

pdf2htmlEX-64×64.png

pdf2htmlEX.js

pdf2htmlEX.js.in

pdf2htmlEX.min.js

images

10_FXX1.jpeg

11_FXX1.jpeg

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9_FXX1.jpeg

text

1.txt

10.txt

11.txt

12.txt

13.txt

14.txt

15.txt

16.txt

17.txt

18.txt

19.txt

2.txt

20.txt

21.txt

22.txt

23.txt

24.txt

25.txt

26.txt

27.txt

28.txt

29.txt

3.txt

30.txt

31.txt

32.txt

33.txt

34.txt

35.txt

36.txt

37.txt

38.txt

39.txt

4.txt

40.txt

41.txt

42.txt

43.txt

44.txt

45.txt

46.txt

47.txt

48.txt

49.txt

5.txt

50.txt

6.txt

7.txt

8.txt

9.txt

ppt

demo.pptx

pic1.jpg

ppt1.py

pptx_usage.pptx

ppt_action.py

word

pic1.jpg

test.docx

word1.py

08-深度学习框架选修课

1.1 tensorflow基础知识以及高级api keras

tensorflow基础知识以及高级apikeras.mp4

深度学习框架.pptx

课堂代码.docx

学习资料

CNN-Alexnet.pdf

GRU.pdf

LSTM.pdf

RNN.pdf

2.1 搭建模型和进阶操作

2-1搭建模型和进阶操作课堂代码.docx

搭建模型和进阶操作.mp4

3.1 tensorflow实践项目“大杂烩”

tensorflow实践项目“大杂烩”.mp4

课堂代码.docx

学习资料

1.jpg

2.jpg

3.jpg

4.png

注意力机制.pdf

自变分编码器.pdf

词向量.pdf

风格迁移.pdf

4.1 pytorch基础知识

pytorch基础知识.mp4

课堂代码.docx

5.1 pytorch神经网络搭建

pytorch神经网络搭建.mp4

stn.pdf

课程代码.docx

09-人工智能基础能力提升课

1.1 编程基础

Allen B. Downey - Think Python (2012, O’Reilly Media) - libgen.lc.pdf

week1-编程基础.mp4

Lesson-01学习资料

article_9k.txt

Lesson-01-course-code-running.ipynb

Lesson-01-Playground.ipynb

Lesson-01-Practice-News-Analysis 答案.zip

Lesson-01-Practice-News-Analysis.ipynb

Lesson-01.pptx

Lesson-1-Course-Source-Code.ipynb

2.1  数据分析基础

week2-数据分析基础.mp4

Lesson-02学习资料

Lesson-02-Online-Course-Code.ipynb

Lesson-02-Playground.ipynb

Lesson-02-text-visuzalization-and-kmeans.ipynb

3.1 机器学习的基本方法

lesson03AIV0.5(2).pptx

week3机器学习的基本方法.mp4

Lesson-03学习资料

jobs_4k.xls

job_analysis.py

L3-code.zip

lesson03AIV0.6.pptx

mnist_clf.py

networkx_graph_demo.py

networkx_pagerank.py

pagerank_simulation.py

random_sample_demo.py

4.1 机器学习的基本方法（二）

Lecture-04-Assignment-Word2vec-Beginning.ipynb

Lesson-04-Numerical-Categorical-and-One-hot-Embedding.ipynb

Lesson-04-word2vec-one-hot.pptx

lesson-04-词向量的过程.ipynb

week4机器学习的基本方法（二）.mp4

5.1 神经网络的基本原理与方法（一）

lesson05AIVV1.1.pptx

week5神经网络的基本原理与方法（一）.mp4

L5

cnn

cnn_feature_map_demo.py

cnn_viz.py

故宫.jpg

cross_entropy

cross_entropy1.py

pytorch

make_moons_pytorch.py

test_pytorch.py

6.1 神经网络的基本原理与方法（二）

lesson06AIV0.8.pptx

week6神经网络的基本原理与方法（二）.mp4

code

mnist_alexnet.py

activation_function

relu1.py

sigmod1.py

tanh1.py

numpy_nn

numpy_boston1.py

numpy_model.py

7.1 卷积神经网络（一）

lesson07AIV1.3.pptx

week7卷积神经网络（一）.mp4

8.1 卷积神经网络（二）

lesson08BIV0.6.pptx

week8卷积神经网络（二）.mp4

AutoML

Bank_Deposit

train.csv

user_request.txt

Flowers

request_api_automl.py

test

image_0719.jpg

image_0720.jpg

image_0799.jpg

image_0800.jpg

image_0879.jpg

image_0880.jpg

train

image_0641.jpg

image_0642.jpg

image_0643.jpg

image_0644.jpg

image_0645.jpg

image_0646.jpg

image_0647.jpg

image_0648.jpg

image_0649.jpg

image_0650.jpg

image_0651.jpg

image_0652.jpg

image_0653.jpg

image_0654.jpg

image_0655.jpg

image_0656.jpg

image_0721.jpg

image_0722.jpg

image_0723.jpg

image_0724.jpg

image_0725.jpg

image_0726.jpg

image_0727.jpg

image_0728.jpg

image_0729.jpg

image_0730.jpg

image_0731.jpg

image_0732.jpg

image_0733.jpg

image_0734.jpg

image_0735.jpg

image_0736.jpg

image_0801.jpg

image_0802.jpg

image_0803.jpg

image_0804.jpg

image_0805.jpg

image_0806.jpg

image_0807.jpg

image_0808.jpg

image_0809.jpg

image_0810.jpg

image_0811.jpg

image_0812.jpg

image_0813.jpg

image_0814.jpg

image_0815.jpg

image_0816.jpg

COVID-CT

dianwei.jpg

random_resized_crop.py

data

COVID

testCT_COVID.txt

trainCT_COVID.txt

valCT_COVID.txt

NonCOVID

CT_NonCOVID_test_id.csv

CT_NonCOVID_train_id.csv

CT_NonCOVID_val_id.csv

testCT_NonCOVID.txt

trainCT_NonCOVID.txt

valCT_NonCOVID.txt

images

CT_COVID

2019-novel-Coronavirus-severe-adult-respiratory-dist_2020_International-Jour-p3-89%0.png

2019-novel-Coronavirus-severe-adult-respiratory-dist_2020_International-Jour-p3-89%1.png

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294.png

3.jpg

30%0.jpg

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31.jpg

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39%0.jpg

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41.jpg

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42.jpg

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43.png

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44.png

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64.jpg

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66.jpg

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67%0.jpg

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67%2.jpg

672.png

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68.jpg

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7.jpg

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tools

dataload.py

ImageDemo

dog.jpg

dog2.jpg

test.py

9.1 图像目标检测

lesson09AIV2.1.pptx

week9图像目标检测.mp4

Object_Detection_Mask

detect.py

LICENSE

makeTxt.py

models.py

requirements.txt

results.png

results.txt

test.py

test_batch0.jpg

train.py

train_batch0.jpg

voc_label.py

cfg

yolov3-1cls.cfg

yolov3-spp.cfg

yolov3-tiny.cfg

yolov3.cfg

data

annotation_replace.py

get_coco_dataset.sh

rbc.data

rbc.names

test.shapes

test.txt

train.txt

trainvalno5k.shapes

val.txt

Annotations

face0.xml

face1.xml

face10.xml

face100.xml

face101.xml

face102.xml

face103.xml

face104.xml

face105.xml

face106.xml

face107.xml

face108.xml

face109.xml

face11.xml

face110.xml

face111.xml

face112.xml

face113.xml

face114.xml

face115.xml

face116.xml

face117.xml

face118.xml

face119.xml

face12.xml

face120.xml

face121.xml

face122.xml

face123.xml

face124.xml

face125.xml

face126.xml

face127.xml

face128.xml

face129.xml

face13.xml

face130.xml

face131.xml

face132.xml

face133.xml

face134.xml

face135.xml

face136.xml

face137.xml

face138.xml

face139.xml

face14.xml

face140.xml

face141.xml

face142.xml

face143.xml

face144.xml

face145.xml

face146.xml

face147.xml

face148.xml

face149.xml

face15.xml

face150.xml

face151.xml

face152.xml

face153.xml

face154.xml

face155.xml

face156.xml

face157.xml

face158.xml

face159.xml

face16.xml

face160.xml

face161.xml

face162.xml

face163.xml

face164.xml

face165.xml

face166.xml

face167.xml

face168.xml

face169.xml

face17.xml

face170.xml

face171.xml

face172.xml

face173.xml

face174.xml

face175.xml

face176.xml

face177.xml

face178.xml

face179.xml

face18.xml

face180.xml

face181.xml

face182.xml

face183.xml

face184.xml

face185.xml

face186.xml

face187.xml

face188.xml

face189.xml

face19.xml

face190.xml

face191.xml

face192.xml

face193.xml

face194.xml

face195.xml

face196.xml

face197.xml

face198.xml

face199.xml

face2.xml

face20.xml

face200.xml

face201.xml

face202.xml

face203.xml

face204.xml

face205.xml

face206.xml

face207.xml

face208.xml

face209.xml

face21.xml

face210.xml

face211.xml

face212.xml

face213.xml

face214.xml

face215.xml

face216.xml

face217.xml

face218.xml

face219.xml

face22.xml

face220.xml

face221.xml

face222.xml

face223.xml

face224.xml

face225.xml

face226.xml

face227.xml

face228.xml

face229.xml

face23.xml

face230.xml

face231.xml

face232.xml

face233.xml

face234.xml

face235.xml

face236.xml

face237.xml

face238.xml

face239.xml

face24.xml

face240.xml

face241.xml

face242.xml

face243.xml

face244.xml

face245.xml

face246.xml

face247.xml

face248.xml

face249.xml

face25.xml

face250.xml

face251.xml

face252.xml

face253.xml

face254.xml

face255.xml

face256.xml

face257.xml

face258.xml

face259.xml

face26.xml

face260.xml

face261.xml

face262.xml

face263.xml

face27.xml

face28.xml

face29.xml

face3.xml

face30.xml

face31.xml

face32.xml

face33.xml

face34.xml

face35.xml

face36.xml

face37.xml

face38.xml

face39.xml

face4.xml

face40.xml

face41.xml

face42.xml

face43.xml

face44.xml

face45.xml

face46.xml

face47.xml

face48.xml

face49.xml

face5.xml

face50.xml

face51.xml

face52.xml

face53.xml

face54.xml

face55.xml

face56.xml

face57.xml

face58.xml

face59.xml

face6.xml

face60.xml

face61.xml

face62.xml

face63.xml

face64.xml

face65.xml

face66.xml

face67.xml

face68.xml

face69.xml

face7.xml

face70.xml

face71.xml

face72.xml

face73.xml

face74.xml

face75.xml

face76.xml

face77.xml

face78.xml

face79.xml

face8.xml

face80.xml

face81.xml

face82.xml

face83.xml

face84.xml

face85.xml

face86.xml

face87.xml

face88.xml

face89.xml

face9.xml

face90.xml

face91.xml

face92.xml

face93.xml

face94.xml

face95.xml

face96.xml

face97.xml

face98.xml

face99.xml

masks_000.xml

masks_001.xml

masks_0010.xml

masks_00100.xml

masks_00101.xml

masks_00102.xml

masks_00103.xml

masks_00104.xml

masks_00105.xml

masks_00106.xml

masks_00107.xml

masks_00108.xml

masks_00109.xml

masks_0011.xml

masks_00110.xml

masks_00111.xml

masks_00112.xml

masks_00113.xml

masks_00114.xml

masks_00115.xml

masks_00116.xml

masks_00117.xml

masks_00118.xml

masks_00119.xml

masks_0012.xml

masks_00120.xml

masks_00121.xml

masks_00122.xml

masks_00123.xml

masks_00124.xml

masks_00125.xml

masks_00126.xml

masks_00127.xml

masks_00128.xml

masks_00129.xml

masks_0013.xml

masks_00130.xml

masks_00131.xml

masks_00132.xml

masks_00133.xml

masks_00134.xml

masks_00135.xml

masks_0014.xml

masks_0015.xml

masks_0016.xml

masks_0017.xml

masks_0018.xml

masks_0019.xml

masks_002.xml

masks_0020.xml

masks_0021.xml

masks_0022.xml

masks_0023.xml

masks_0024.xml

masks_0025.xml

masks_0026.xml

masks_0027.xml

masks_0028.xml

masks_0029.xml

masks_003.xml

masks_0030.xml

masks_0031.xml

masks_0032.xml

masks_0033.xml

masks_0034.xml

masks_0035.xml

masks_0036.xml

masks_0037.xml

masks_0038.xml

masks_0039.xml

masks_004.xml

masks_0040.xml

masks_0041.xml

masks_0042.xml

masks_0043.xml

masks_0044.xml

masks_0045.xml

masks_0046.xml

masks_0047.xml

masks_0048.xml

masks_0049.xml

masks_005.xml

masks_0050.xml

masks_0051.xml

masks_0052.xml

masks_0053.xml

masks_0054.xml

masks_0055.xml

masks_0056.xml

masks_0057.xml

masks_0058.xml

masks_0059.xml

masks_006.xml

masks_0060.xml

masks_0061.xml

masks_0062.xml

masks_0063.xml

masks_0064.xml

masks_0065.xml

masks_0066.xml

masks_0067.xml

masks_0068.xml

masks_0069.xml

masks_007.xml

masks_0070.xml

masks_0071.xml

masks_0072.xml

masks_0073.xml

masks_0074.xml

masks_0075.xml

masks_0076.xml

masks_0077.xml

masks_0078.xml

masks_0079.xml

masks_008.xml

masks_0080.xml

masks_0081.xml

masks_0082.xml

masks_0083.xml

masks_0084.xml

masks_0085.xml

masks_0086.xml

masks_0087.xml

masks_0088.xml

masks_0089.xml

masks_009.xml

masks_0090.xml

masks_0091.xml

masks_0092.xml

masks_0093.xml

masks_0094.xml

masks_0095.xml

masks_0096.xml

masks_0097.xml

masks_0098.xml

masks_0099.xml

images

face0.jpg

face1.jpg

face10.jpg

face100.jpg

face101.jpg

face102.jpg

face103.jpg

face104.jpg

face105.jpg

face106.jpg

face107.jpg

face108.jpg

face109.jpg

face11.jpg

face110.jpg

face111.jpg

face112.jpg

face113.jpg

face114.jpg

face115.jpg

face116.jpg

face117.jpg

face118.jpg

face119.jpg

face12.jpg

face120.jpg

face121.jpg

face122.jpg

face123.jpg

face124.jpg

face125.jpg

face126.jpg

face127.jpg

face128.jpg

face129.jpg

face13.jpg

face130.jpg

face131.jpg

face132.jpg

face133.jpg

face134.jpg

face135.jpg

face136.jpg

face137.jpg

face138.jpg

face139.jpg

face14.jpg

face140.jpg

face141.jpg

face142.jpg

face143.jpg

face144.jpg

face145.jpg

face146.jpg

face147.jpg

face148.jpg

face149.jpg

face15.jpg

face150.jpg

face151.jpg

face152.jpg

face153.jpg

face154.jpg

face155.jpg

face156.jpg

face157.jpg

face158.jpg

face159.jpg

face16.jpg

face160.jpg

face161.jpg

face162.jpg

face163.jpg

face164.jpg

face165.jpg

face166.jpg

face167.jpg

face168.jpg

face169.jpg

face17.jpg

face170.jpg

face171.jpg

face172.jpg

face173.jpg

face174.jpg

face175.jpg

face176.jpg

face177.jpg

face178.jpg

face179.jpg

face18.jpg

face180.jpg

face181.jpg

face182.jpg

face183.jpg

face184.jpg

face185.jpg

face186.jpg

face187.jpg

face188.jpg

face189.jpg

face19.jpg

face190.jpg

face191.jpg

face192.jpg

face193.jpg

face194.jpg

face195.jpg

face196.jpg

face197.jpg

face198.jpg

face199.jpg

face2.jpg

face20.jpg

face200.jpg

face201.jpg

face202.jpg

face203.jpg

face204.jpg

face205.jpg

face206.jpg

face207.jpg

face208.jpg

face209.jpg

face21.jpg

face210.jpg

face211.jpg

face212.jpg

face213.jpg

face214.jpg

face215.jpg

face216.jpg

face217.jpg

face218.jpg

face219.jpg

face22.jpg

face220.jpg

face221.jpg

face222.jpg

face223.jpg

face224.jpg

face225.jpg

face226.jpg

face227.jpg

face228.jpg

face229.jpg

face23.jpg

face230.jpg

face231.jpg

face232.jpg

face233.jpg

face234.jpg

face235.jpg

face236.jpg

face237.jpg

face238.jpg

face239.jpg

face24.jpg

face240.jpg

face241.jpg

face242.jpg

face243.jpg

face244.jpg

face245.jpg

face246.jpg

face247.jpg

face248.jpg

face249.jpg

face25.jpg

face250.jpg

face251.jpg

face252.jpg

face253.jpg

face254.jpg

face255.jpg

face256.jpg

face257.jpg

face258.jpg

face259.jpg

face26.jpg

face260.jpg

face261.jpg

face262.jpg

face263.jpg

face27.jpg

face28.jpg

face29.jpg

face3.jpg

face30.jpg

face31.jpg

face32.jpg

face33.jpg

face34.jpg

face35.jpg

face36.jpg

face37.jpg

face38.jpg

face39.jpg

face4.jpg

face40.jpg

face41.jpg

face42.jpg

face43.jpg

face44.jpg

face45.jpg

face46.jpg

face47.jpg

face48.jpg

face49.jpg

face5.jpg

face50.jpg

face51.jpg

face52.jpg

face53.jpg

face54.jpg

face55.jpg

face56.jpg

face57.jpg

face58.jpg

face59.jpg

face6.jpg

face60.jpg

face61.jpg

face62.jpg

face63.jpg

face64.jpg

face65.jpg

face66.jpg

face67.jpg

face68.jpg

face69.jpg

face7.jpg

face70.jpg

face71.jpg

face72.jpg

face73.jpg

face74.jpg

face75.jpg

face76.jpg

face77.jpg

face78.jpg

face79.jpg

face8.jpg

face80.jpg

face81.jpg

face82.jpg

face83.jpg

face84.jpg

face85.jpg

face86.jpg

face87.jpg

face88.jpg

face89.jpg

face9.jpg

face90.jpg

face91.jpg

face92.jpg

face93.jpg

face94.jpg

face95.jpg

face96.jpg

face97.jpg

face98.jpg

face99.jpg

masks_000.jpg

masks_001.jpg

masks_0010.jpg

masks_00100.jpg

masks_00101.jpg

masks_00102.jpg

masks_00103.jpg

masks_00104.jpg

masks_00105.jpg

masks_00106.jpg

masks_00107.jpg

masks_00108.jpg

masks_00109.jpg

masks_0011.jpg

masks_00110.jpg

masks_00111.jpg

masks_00112.jpg

masks_00113.jpg

masks_00114.jpg

masks_00115.jpg

masks_00116.jpg

masks_00117.jpg

masks_00118.jpg

masks_00119.jpg

masks_0012.jpg

masks_00120.jpg

masks_00121.jpg

masks_00122.jpg

masks_00123.jpg

masks_00124.jpg

masks_00125.jpg

masks_00126.jpg

masks_00127.jpg

masks_00128.jpg

masks_00129.jpg

masks_0013.jpg

masks_00130.jpg

masks_00131.jpg

masks_00132.jpg

masks_00133.jpg

masks_00134.jpg

masks_00135.jpg

masks_0014.jpg

masks_0015.jpg

masks_0016.jpg

masks_0017.jpg

masks_0018.jpg

masks_0019.jpg

masks_002.jpg

masks_0020.jpg

masks_0021.jpg

masks_0022.jpg

masks_0023.jpg

masks_0024.jpg

masks_0025.jpg

masks_0026.jpg

masks_0027.jpg

masks_0028.jpg

masks_0029.jpg

masks_003.jpg

masks_0030.jpg

masks_0031.jpg

masks_0032.jpg

masks_0033.jpg

masks_0034.jpg

masks_0035.jpg

masks_0036.jpg

masks_0037.jpg

masks_0038.jpg

masks_0039.jpg

masks_004.jpg

masks_0040.jpg

masks_0041.jpg

masks_0042.jpg

masks_0043.jpg

masks_0044.jpg

masks_0045.jpg

masks_0046.jpg

masks_0047.jpg

masks_0048.jpg

masks_0049.jpg

masks_005.jpg

masks_0050.jpg

masks_0051.jpg

masks_0052.jpg

masks_0053.jpg

masks_0054.jpg

masks_0055.jpg

masks_0056.jpg

masks_0057.jpg

masks_0058.jpg

masks_0059.jpg

masks_006.jpg

masks_0060.jpg

masks_0061.jpg

masks_0062.jpg

masks_0063.jpg

masks_0064.jpg

masks_0065.jpg

masks_0066.jpg

masks_0067.jpg

masks_0068.jpg

masks_0069.jpg

masks_007.jpg

masks_0070.jpg

masks_0071.jpg

masks_0072.jpg

masks_0073.jpg

masks_0074.jpg

masks_0075.jpg

masks_0076.jpg

masks_0077.jpg

masks_0078.jpg

masks_0079.jpg

masks_008.jpg

masks_0080.jpg

masks_0081.jpg

masks_0082.jpg

masks_0083.jpg

masks_0084.jpg

masks_0085.jpg

masks_0086.jpg

masks_0087.jpg

masks_0088.jpg

masks_0089.jpg

masks_009.jpg

masks_0090.jpg

masks_0091.jpg

masks_0092.jpg

masks_0093.jpg

masks_0094.jpg

masks_0095.jpg

masks_0096.jpg

masks_0097.jpg

masks_0098.jpg

masks_0099.jpg

JPEGImages

face0.jpg

face1.jpg

face10.jpg

face100.jpg

face101.jpg

face102.jpg

face103.jpg

face104.jpg

face105.jpg

face106.jpg

face107.jpg

face108.jpg

face109.jpg

face11.jpg

face110.jpg

face111.jpg

face112.jpg

face113.jpg

face114.jpg

face115.jpg

face116.jpg

face117.jpg

face118.jpg

face119.jpg

face12.jpg

face120.jpg

face121.jpg

face122.jpg

face123.jpg

face124.jpg

face125.jpg

face126.jpg

face127.jpg

face128.jpg

face129.jpg

face13.jpg

face130.jpg

face131.jpg

face132.jpg

face133.jpg

face134.jpg

face135.jpg

face136.jpg

face137.jpg

face138.jpg

face139.jpg

face14.jpg

face140.jpg

face141.jpg

face142.jpg

face143.jpg

face144.jpg

face145.jpg

face146.jpg

face147.jpg

face148.jpg

face149.jpg

face15.jpg

face150.jpg

face151.jpg

face152.jpg

face153.jpg

face154.jpg

face155.jpg

face156.jpg

face157.jpg

face158.jpg

face159.jpg

face16.jpg

face160.jpg

face161.jpg

face162.jpg

face163.jpg

face164.jpg

face165.jpg

face166.jpg

face167.jpg

face168.jpg

face169.jpg

face17.jpg

face170.jpg

face171.jpg

face172.jpg

face173.jpg

face174.jpg

face175.jpg

face176.jpg

face177.jpg

face178.jpg

face179.jpg

face18.jpg

face180.jpg

face181.jpg

face182.jpg

face183.jpg

face184.jpg

face185.jpg

face186.jpg

face187.jpg

face188.jpg

face189.jpg

face19.jpg

face190.jpg

face191.jpg

face192.jpg

face193.jpg

face194.jpg

face195.jpg

face196.jpg

face197.jpg

face198.jpg

face199.jpg

face2.jpg

face20.jpg

face200.jpg

face201.jpg

face202.jpg

face203.jpg

face204.jpg

face205.jpg

face206.jpg

face207.jpg

face208.jpg

face209.jpg

face21.jpg

face210.jpg

face211.jpg

face212.jpg

face213.jpg

face214.jpg

face215.jpg

face216.jpg

face217.jpg

face218.jpg

face219.jpg

face22.jpg

face220.jpg

face221.jpg

face222.jpg

face223.jpg

face224.jpg

face225.jpg

face226.jpg

face227.jpg

face228.jpg

face229.jpg

face23.jpg

face230.jpg

face231.jpg

face232.jpg

face233.jpg

face234.jpg

face235.jpg

face236.jpg

face237.jpg

face238.jpg

face239.jpg

face24.jpg

face240.jpg

face241.jpg

face242.jpg

face243.jpg

face244.jpg

face245.jpg

face246.jpg

face247.jpg

face248.jpg

face249.jpg

face25.jpg

face250.jpg

face251.jpg

face252.jpg

face253.jpg

face254.jpg

face255.jpg

face256.jpg

face257.jpg

face258.jpg

face259.jpg

face26.jpg

face260.jpg

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masks_0016.jpg

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masks_0019.jpg

masks_002.jpg

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masks_0022.jpg

masks_0023.jpg

masks_0024.jpg

masks_0025.jpg

masks_0026.jpg

masks_0027.jpg

masks_0028.jpg

masks_0029.jpg

masks_003.jpg

masks_0030.jpg

masks_0031.jpg

masks_0032.jpg

masks_0033.jpg

masks_0034.jpg

masks_0035.jpg

masks_0036.jpg

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masks_0038.jpg

masks_0039.jpg

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masks_0090.jpg

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masks_0092.jpg

masks_0093.jpg

masks_0094.jpg

masks_0095.jpg

masks_0096.jpg

masks_0097.jpg

masks_0098.jpg

masks_0099.jpg

labels

face0.txt

face1.txt

face10.txt

face100.txt

face101.txt

face102.txt

face103.txt

face104.txt

face105.txt

face106.txt

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face139.txt

face14.txt

face140.txt

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face142.txt

face143.txt

face144.txt

face145.txt

face146.txt

face147.txt

face148.txt

face149.txt

face15.txt

face150.txt

face151.txt

face152.txt

face153.txt

face154.txt

face155.txt

face156.txt

face157.txt

face158.txt

face159.txt

face16.txt

face160.txt

face161.txt

face162.txt

face163.txt

face164.txt

face165.txt

face166.txt

face167.txt

face168.txt

face169.txt

face17.txt

face170.txt

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face172.txt

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face175.txt

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face180.txt

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face183.txt

face184.txt

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face186.txt

face187.txt

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face190.txt

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face192.txt

face193.txt

face194.txt

face195.txt

face196.txt

face197.txt

face198.txt

face199.txt

face2.txt

face20.txt

face200.txt

face201.txt

face202.txt

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face204.txt

face205.txt

face206.txt

face207.txt

face208.txt

face209.txt

face21.txt

face210.txt

face211.txt

face212.txt

face213.txt

face214.txt

face215.txt

face216.txt

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face218.txt

face219.txt

face22.txt

face220.txt

face221.txt

face222.txt

face223.txt

face224.txt

face225.txt

face226.txt

face227.txt

face228.txt

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face23.txt

face230.txt

face231.txt

face232.txt

face233.txt

face234.txt

face235.txt

face236.txt

face237.txt

face238.txt

face239.txt

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face240.txt

face241.txt

face242.txt

face243.txt

face244.txt

face245.txt

face246.txt

face247.txt

face248.txt

face249.txt

face25.txt

face250.txt

face251.txt

face252.txt

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face254.txt

face255.txt

face256.txt

face257.txt

face258.txt

face259.txt

face26.txt

face260.txt

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face262.txt

face263.txt

face27.txt

face28.txt

face29.txt

face3.txt

face30.txt

face31.txt

face32.txt

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face34.txt

face35.txt

face36.txt

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face38.txt

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face4.txt

face40.txt

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face42.txt

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face44.txt

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face46.txt

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face48.txt

face49.txt

face5.txt

face50.txt

face51.txt

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face54.txt

face55.txt

face56.txt

face57.txt

face58.txt

face59.txt

face6.txt

face60.txt

face61.txt

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face90.txt

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face92.txt

face93.txt

face94.txt

face95.txt

face96.txt

face97.txt

face98.txt

face99.txt

masks_000.txt

masks_001.txt

masks_0010.txt

masks_00100.txt

masks_00101.txt

masks_00102.txt

masks_00103.txt

masks_00104.txt

masks_00105.txt

masks_00106.txt

masks_00107.txt

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masks_0011.txt

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masks_00113.txt

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masks_00115.txt

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masks_0023.txt

masks_0024.txt

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masks_0026.txt

masks_0027.txt

masks_0028.txt

masks_0029.txt

masks_003.txt

masks_0030.txt

masks_0031.txt

masks_0032.txt

masks_0033.txt

masks_0034.txt

masks_0035.txt

masks_0036.txt

masks_0037.txt

masks_0038.txt

masks_0039.txt

masks_004.txt

masks_0040.txt

masks_0041.txt

masks_0042.txt

masks_0043.txt

masks_0044.txt

masks_0045.txt

masks_0046.txt

masks_0047.txt

masks_0048.txt

masks_0049.txt

masks_005.txt

masks_0050.txt

masks_0051.txt

masks_0052.txt

masks_0053.txt

masks_0054.txt

masks_0055.txt

masks_0056.txt

masks_0057.txt

masks_0058.txt

masks_0059.txt

masks_006.txt

masks_0060.txt

masks_0061.txt

masks_0062.txt

masks_0063.txt

masks_0064.txt

masks_0065.txt

masks_0066.txt

masks_0067.txt

masks_0068.txt

masks_0069.txt

masks_007.txt

masks_0070.txt

masks_0071.txt

masks_0072.txt

masks_0073.txt

masks_0074.txt

masks_0075.txt

masks_0076.txt

masks_0077.txt

masks_0078.txt

masks_0079.txt

masks_008.txt

masks_0080.txt

masks_0081.txt

masks_0082.txt

masks_0083.txt

masks_0084.txt

masks_0085.txt

masks_0086.txt

masks_0087.txt

masks_0088.txt

masks_0089.txt

masks_009.txt

masks_0090.txt

masks_0091.txt

masks_0092.txt

masks_0093.txt

masks_0094.txt

masks_0095.txt

masks_0096.txt

masks_0097.txt

masks_0098.txt

masks_0099.txt

samples

face6.jpg

face69.jpg

face7.jpg

masks19.jpg

masks_0012.jpg

masks_0039.jpg

masks_0057.jpg

masks_0059.jpg

output

face6.jpg

face69.jpg

face7.jpg

masks19.jpg

masks_0012.jpg

masks_0039.jpg

masks_0057.jpg

masks_0059.jpg

project

datasets.py

gcp.sh

parse_config.py

torch_utils.py

utils.py

weights

best.pt

download_yolov3_weights.sh

latest.pt

yolov3-tiny.conv.15

yolov3-tiny.weights

10-公开课

公开课-AI算法工程师被裁的原因是什么？-20210127.mp4

目录

01、第一章 人工智能导论

Github简明教程.pdf

人工智能导论–课程代码.zip

人工智能导论.pptx

第一章第1节： 人工智能导论.mp4

02、第二章 机器学习初探

abcnews-date-text(1).zip

k-means-clustering-of-1-million-headlines.zip

Lecture-02(1).pptx

lecture-02.zip

机器学习初探–课程代码.zip

第二章第1节： 机器学习初探.mp4

03、第三章 机器学习一

机器学习一.pptx

第三章第1节： 机器学习一.mp4

课堂代码.zip

04、第四章 机器学习二

heart.zip

Lecture-04(1).pptx

第四章-逻辑回归诊断心脏病 (1).zip

第四章-逻辑回归诊断心脏病.zip

第四章第1节： 机器学习二.mp4

课程代码.zip

05、第五章 机器学习三

ccf_offline_stage1_train.zip

机器学习3.pptx

第五章–决策树.zip

第五章第1节： 机器学习三.mp4

课堂代码(1).zip

06、第六章 深度学习初步

Lecture-06.pptx

Lecture-06.zip

mnist_test.zip

mnist_train.zip

第六章 – ensemble.zip

第六章第1节： 深度学习初步.mp4

07、第七章 深度学习进阶

Lecture-07(2).zip

Lecture-07.pptx

第七章–深度学习进阶.zip

第七章第1节： 深度学习进阶.mp4

08、第八章 RNN

Lecture-08(1).pptx

RNN课堂代码.zip

第八章–LSTM结构.zip

第八章第1节： RNN.mp4

09、第九章 CNN

Lecture-09.pptx

lecture-09.zip

数据文件.zip

第九章 – 验证码识别.zip

第九章第1节： CNN.mp4

10、第十章 自然语言处理

Lecture-10.pptx

lecture-10.zip

作业.docx

第一十章第1节： 自然语言处理.mp4

11、第十一章 计算机视觉CV

week11作业文档.docx

开课吧AI核心课CV概论资料包.zip

第一十一章第1节： 计算机视觉CV.mp4

12、第十二章 商业智能BI

BI预习资料0401.pdf

第一十二章第1节： 商业智能BI.mp4

13、第十三章 作业讲解

第一十三章第1节： 作业讲解.mp4

课堂代码(1).zip

14、第十四章 新增视频

1.pptx

2020-神经网络框架原理训练营资料.zip

Build-neural-network-from-scrach-Lesson-02.zip

Lesson-01-代码复现与参考答案.zip

Lesson-03.zip

第一十四章第1节： 新增视频（一）.mp4

第一十四章第2节： 新增视频（二）.mp4

第一十四章第3节： 新增视频（三）.mp4

课程目录

01、第一章 自然语言处理的基本过程

第一章第1节： 自然语言处理的基本过程.mp4

自然语言处理的基本过程–作业.zip

自然语言处理的基本过程–标准答案.zip

自然语言处理的基本过程.pptx.zip

02、第二章 向量空间模型

1_向量空间模型.pptx.zip

作业 – 使用 PCA 进行降维可视化.zip

作业-答案.zip

第二章第1节： 向量空间模型.mp4

03、第三章 自然语言理解初步

realdonaldtrump.csv.zip

作业.docx

第三章第1节： 自然语言理解初步.mp4

自然语言处理初步.pptx.zip

04、第四章 语言模型与概率图模型

CRF_NER.zip

ner_dataset.csv.zip

作业文档.docx

第四章第1节： 语言模型与概率图模型.mp4

语言模型和概率图模型.pptx

05、第五章 词向量模型Word2Vec

1_词向量.pptx.zip

作业(1).docx

第五章第1节： 词向量模型Word2Vec.mp4

06、第六章 Transformer与BERT，大规模预训练问题

大规模预训练问题 – 作业题.ipynb.zip

第六章第1节： Transformer与BERT，大规模预训练问题.mp4

预训练模型.pptx

07、第七章 自然语言生成

作业(2).docx

文本生成.pptx.zip

第七章第1节： 自然语言生成.mp4

08、第八章 自然语言处理与人工智能前沿