小薇 机器学习+深度学习【11套课程】
课程介绍：
课程资源名称：机器学习+深度学习【11套课程】，资源大小：47.52G，详见下发截图与文件目录。
课程文件目录：机器学习+深度学习【11套课程】[47.52G]
【备战秋招】面试刷题+算法强化训练营第三期（完结） [3.09G]
视频 [3.09G]
【svm】smo算法【,..ts [144.04M]
【svm】svm最优化问题【,】..ts [44.13M]
【svm】核函数【,..ts [86.35M]
【svm】几个重要的概念【 ..ts [24.71M]
【svm】线性可分svm【,】..ts [73.61M]
02-前向传播】..ts [54.44M]
03-损失函数选用..ts [28.82M]
04-反向传播1【,】..ts [87.54M]
05-反向传播2【,】..ts [63.20M]
1.ts [37.42M]
110.ts [19.39M]
144.ts [22.14M]
160.ts [35.26M]
167.ts [36.47M]
169.ts [12.59M]
17.ts [15.77M]
2.均匀采样、逆变换采样、拒绝采样【,】..ts [27.73M]
2.梯度下降简单的数学原理【,】..ts [30.55M]
208.ts [24.94M]
215.ts [62.40M]
230.ts [7.17M]
232.ts [39.20M]
241.ts [40.23M]
242.ts [55.88M]
260.ts [22.98M]
279.ts [34.41M]
3.mcmc采样【买,】..ts [56.54M]
3.随机梯度下降和小批量随机梯度下降【,】..ts [15.71M]
303.ts [7.23M]
309.ts [15.11M]
343.ts [11.81M]
347.ts [61.62M]
378.ts [23.77M]
409.ts [8.83M]
416.ts [17.40M]
455.ts [59.92M]
462.ts [10.16M]
5.常见的一些改进的优化算法【,】..ts [40.92M]
504.ts [5.49M]
513.ts [7.66M]
583.ts [9.78M]
64【,】..ts [19.57M]
69【,】..ts [41.79M]
695【,】..ts [18.89M]
70【,】..ts [10.34M]
75【,】..ts [51.54M]
crf的一些基础概念【,】..ts [90.33M]
crf具体介绍【,】..ts [113.08M]
gru＆lstm【,..ts [30.16M]
gru和lstm【,..ts [13.50M]
hmm【,..ts [41.34M]
hmm的引出和问题的介绍【,.ts [41.34M]
hmm预测问题之维特比算法【,..ts [139.48M]
k-means【,..ts [67.13M]
pca和lda【,..ts [76.64M]
rnn【,】..ts [25.59M]
采样【买,】..ts [44.91M]
动量法【,】..ts [34.01M]
吉布斯采样【,】..ts [23.71M]
决策树【,】..ts [47.14M]
开营仪式——班主任部分【,】..ts [170.46M]
开营仪式——老师部分,】..ts [378.43M]
逻辑回归【,..ts [43.79M]
深度学习中的优化问题【,】..ts [23.02M]
绪论【买,..ts [30.23M]
硬间隔svm最优化问题的推导【,..ts [108.36M]
资料 [17.85K]
第二周：学习支持向量机【买课程】..txt [3.50K]
第三周：了解机器学习中的非监督学习算法【买课程】..txt [3.62K]
第四周：了解优化算法的原理【买买课程】..txt [3.04K]
第五周：学习前向神经【买课程】..txt [3.90K]
第一周：了解机器学习中的特征工程和模型评估【买课程】..txt [3.79K]
cnn_不能错过的10篇论文 [65.26M]
1311.2524v5_r_cnn.pdf [6.23M]
1311.2901v3_visualizing and understanding convolutional networks.pdf [34.56M]
1406.2661v1_generative adversarial nets.pdf [518.05K]
1409.1556v6_very deep convolutional networks.pdf [195.32K]
1412.2306v2_deep visual-semantic alignments for generating image descriptions.pdf [5.21M]
1504.08083_fast r-cnn.pdf [713.99K]
1506.01497v3_faster r-cnn.pdf [6.59M]
1506.02025_spatial transformer networks.pdf [7.89M]
1512.03385v1_deep residual learning for image recognition.pdf [800.18K]
4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf [1.35M]
szegedy_going_deeper_with_2015_cvpr_paper.pdf [1.24M]
cs224n 2019 [7.00G]
assignment [164.67M]
a1 [102.35K]
imgs [59.52K]
inner_product.png [16.00K]
svd.png [10.18K]
test_plot.png [33.34K]
exploring_word_vectors(1).ipynb [42.14K]
readme.txt [0.70K]
a2 [317.56K]
utils 买课程v,zszhp2019 [16.00K]
.ds_store [6.00K]
__init__.py
gradcheck.py [1.60K]
treebank.py [7.37K]
utils.py [1.03K]
collect_submission.sh [0.08K]
env.yml [0.13K]
get_datasets.sh [0.39K]
run.py [2.23K]
sgd.py [3.40K]
word2vec.py [8.93K]
a3 [123.18M]
data [122.78M]
dev.conll [1.25M]
dev.gold.conll [1.25M]
en-cw.txt [57.73M]
test.conll [1.76M]
test.gold.conll [1.76M]
train.conll [29.52M]
train.gold.conll [29.52M]
utils [53.84K]
__pycache__ [35.55K]
__init__.cpython-36.pyc [0.16K]
__init__.cpython-37.pyc [0.17K]
general_utils.cpython-36.pyc [2.53K]
general_utils.cpython-37.pyc [2.66K]
parser_utils.cpython-36.pyc [14.72K]
parser_utils.cpython-37.pyc [15.32K]
__init__.py
general_utils.py [2.39K]
parser_utils.py [15.90K]
.ds_store [6.00K]
collect_submission.sh [0.06K]
parser_model.py [7.46K]
parser_transitions.py [9.05K]
run.py [5.58K]
a4 [40.07M]
en_es_data [39.63M]
dev.en [83.80K]
dev.es [84.35K]
test.en [714.46K]
test.es [700.02K]
train.en [19.03M]
train.es [19.06M]
sanity_check_en_es_data [39.17K]
combined_outputs.pkl [1.51K]
dec_init_state.pkl [0.55K]
dec_state.pkl [0.55K]
e_t.pkl [0.73K]
enc_hiddens.pkl [2.68K]
enc_hiddens_proj.pkl [1.51K]
enc_masks.pkl [0.72K]
o_t.pkl [0.39K]
step_dec_state_0.pkl [0.55K]
step_dec_state_1.pkl [0.55K]
step_dec_state_10.pkl [0.55K]
step_dec_state_11.pkl [0.55K]
step_dec_state_12.pkl [0.55K]
step_dec_state_13.pkl [0.55K]
step_dec_state_14.pkl [0.55K]
step_dec_state_15.pkl [0.55K]
step_dec_state_16.pkl [0.55K]
step_dec_state_17.pkl [0.55K]
step_dec_state_18.pkl [0.55K]
step_dec_state_19.pkl [0.55K]
step_dec_state_2.pkl [0.55K]
step_dec_state_3.pkl [0.55K]
step_dec_state_4.pkl [0.55K]
step_dec_state_5.pkl [0.55K]
step_dec_state_6.pkl [0.55K]
step_dec_state_7.pkl [0.55K]
step_dec_state_8.pkl [0.55K]
step_dec_state_9.pkl [0.55K]
step_o_t_0.pkl [0.39K]
step_o_t_1.pkl [0.39K]
step_o_t_10.pkl [0.39K]
step_o_t_11.pkl [0.39K]
step_o_t_12.pkl [0.39K]
step_o_t_13.pkl [0.39K]
step_o_t_14.pkl [0.39K]
step_o_t_15.pkl [0.39K]
step_o_t_16.pkl [0.39K]
step_o_t_17.pkl [0.39K]
step_o_t_18.pkl [0.39K]
step_o_t_19.pkl [0.39K]
step_o_t_2.pkl [0.39K]
step_o_t_3.pkl [0.39K]
step_o_t_4.pkl [0.39K]
step_o_t_5.pkl [0.39K]
step_o_t_6.pkl [0.39K]
step_o_t_7.pkl [0.39K]
step_o_t_8.pkl [0.39K]
step_o_t_9.pkl [0.39K]
target_padded.pkl [1.15K]
train_sanity_check.en [3.86K]
train_sanity_check.es [3.77K]
vocab_sanity_check.json [2.51K]
ybar_t.pkl [0.45K]
.ds_store [10.00K]
__init__.py
collect_submission.sh [0.10K]
gpu_requirements.txt [0.02K]
local_env.yml [0.15K]
model_embeddings.py [1.81K]
nmt_model.py [24.67K]
readme.md [0.09K]
run.py [14.25K]
run.sh [0.89K]
sanity_check.py [9.03K]
utils.py [2.30K]
vocab.py [7.93K]
a2.pdf [286.41K]
a3.pdf [319.41K]
a4.pdf [339.20K]
a5.pdf [431.17K]
default-final-project-handout.pdf [605.74K]
lecture [372.41M]
lecture 01 introduction and word vectors [9.14M]
assignment 1 [60.51K]
a1.zip [60.43K]
preview.txt [0.09K]
gensim word vectors example [2.19K]
gensim.zip [2.10K]
preview.txt [0.09K]
suggested readings [332.80K]
5021-distributed-representations-of-words-and-phrases-and-their-compositionality.pdf [109.37K]
efficient estimation of word representations in vector space.pdf [223.36K]
word2vec tutorial – the skip-gram model.txt [0.07K]
cs224n-2019-lecture01-wordvecs1.pdf [8.41M]
cs224n-2019-notes01-wordvecs1.pdf [353.98K]
lecture 02 word vectors 2 and word senses [22.52M]
additional readings [3.01M]
a latent variable model approach to pmi-based word embeddings.pdf [1.42M]
linear algebraic structure of word senses, with applications to polysemy.pdf [820.01K]
on the dimensionality of word embedding..pdf [809.25K]
suggested readings [3.05M]
evaluation methods for unsupervised word embeddings.pdf [280.05K]
glove global vectors for word representation.pdf [2.50M]
improving distributional similarity with lessons learned from word embeddings.pdf [284.29K]
cs224n-2019-lecture02-wordvecs2.pdf [16.00M]
cs224n-2019-notes02-wordvecs2.pdf [472.57K]
lecture 03 word window classification, neural networks, and matrix calculus [15.79M]
additional readings [414.82K]
natural language processing (almost) from scratch.pdf [414.82K]
assignment 2 [527.53K]
a2(1).zip [10.58K]
a2.pdf [286.41K]
cs224n_ practical tips for using virtual machines.pdf [230.55K]
suggested readings [139.00K]
cs231n notes on backprop.txt [0.04K]
review of differential calculus.pdf [138.96K]
cs224n-2019-lecture03-neuralnets.pdf [13.97M]
cs224n-2019-notes03-neuralnets.pdf [584.62K]
matrix calculus notes.pdf [197.96K]
lecture 04 backpropagation and computation graphs [12.19M]
suggested readings [543.95K]
cs231n notes on network architectures.txt [0.04K]
derivatives, backpropagation, and vectorization.pdf [201.07K]
learning representations by backpropagating errors.pdf [342.76K]
yes you should understand backprop.txt [0.07K]
cs224n-2019-lecture04-backprop.pdf [11.09M]
cs224n-2019-notes03-neuralnets.pdf [584.62K]
lecture 05 linguistic structure dependency parsing [75.22M]
assignment 3 [37.42M]
a3 加，.pdf [319.41K]
a3.zip [37.11M]
suggested readings [910.35K]
a fast and accurate dependency parser using neural networks.pdf [578.78K]
dependency parsing(1).txt [0.07K]
globally normalized transition-based neural networks.pdf [168.02K]
universal dependencies website.txt [0.03K]
universal stanford dependencies a cross-linguistic typology.pdf [163.44K]
cs224n-2019-lecture05-dep-parsing.pdf [16.54M]
cs224n-2019-lecture05-dep-parsing-scrawls.pdf [20.18M]
cs224n-2019-notes04-dependencyparsing.pdf [187.45K]
lecture 06 the probability of a sentence recurrent neural networks and language models [3.65M]
suggested readings [241.55K]
n-gram language models.pdf [241.42K]
on chomsky and the two cultures of statistical learning.txt [0.03K]
sequence modeling recurrent and recursive neural nets.txt [0.05K]
the unreasonable effectiveness of recurrent neural networks.txt [0.05K]
cs224n-2019-lecture06-rnnlm.pdf [1.99M]
cs224n-2019-notes05-lm_rnn.pdf [1.42M]
lecture 07 vanishing gradients and fancy rnns [21.16M]
assignment 4 [16.74M]
a4.pdf [339.20K]
a4.zip [14.39M]
azure guide for cs224n.pdf [1.79M]
cs224n_ practical tips for using virtual machines.pdf [230.55K]
suggested readings [1.58M]
learning long-term dependencies with gradient descent is difficult.pdf [0.98M]
on the difficulty of training recurrent neural networks.pdf [610.95K]
sequence modeling recurrent and recursive neural nets.txt [0.05K]
understanding lstm networks.txt [0.06K]
vanishing gradients jupyter notebook.txt [0.09K]
cs224n-2019-lecture07-fancy-rnn.pdf [1.42M]
cs224n-2019-notes05-lm_rnn.pdf [1.42M]
lecture 08 machine translation, seq2seq and attention [5.26M]
suggested readings [2.43M]
attention and augmented recurrent neural networks.txt [0.04K]
bleu.pdf [275.44K]
massive exploration of neural machine translation architectures .pdf [298.80K]
neural machine translation by jointly learning to align and translate.pdf [434.06K]
sequence to sequence learning with neural networks.pdf [109.46K]
sequence transduction with recurrent neural networks.pdf [1.34M]
statistical machine translation (book by philipp koehn).txt [0.10K]
statistical machine translation slides, cs224n 2015 (lectures 2 3 4).txt [0.07K]
cs224n-2019-lecture08-nmt.pdf [2.27M]
cs224n-2019-notes06-nmt_seq2seq_attention.pdf [580.39K]
lecture 09 practical tips for final projects [19.56M]
cs224n-2019-lecture09-final-projects.pdf [19.38M]
final-project-practical-tips.pdf [182.58K]
practical methodology.txt [0.06K]
lecture 10 question answering and the default final project [15.21M]
default final project [605.77K]
default-final-project-handout.pdf [605.74K]
github repo.txt [0.03K]
project proposal [105.67K]
project-proposal-instructions.pdf [105.67K]
cs224n-2019-lecture10-qa.pdf [14.51M]
lecture 11 convnets for nlp 加，zszhp2019 [16.96M]
suggested readings [653.39K]
a convolutional neural network for modelling sentences.pdf [417.42K]
convolutional neural networks for sentence classification.pdf [235.97K]
cs224n-2019-lecture12-subwords.pdf [16.32M]
lecture 12 information from parts of words subword models [16.74M]
assignment 5 [431.23K]
a5.pdf [431.17K]
zip (requires stanford login).txt [0.06K]
cs224n-2019-lecture12-subwords.pdf [16.32M]
lecture 13 modeling contexts of use contextual representations and pretraining [23.76M]
suggested readings [121.00K]
contextual word representations a contextual introduction.pdf [121.00K]
contextual word representations a contextual introduction.pdf [121.00K]
cs224n-2019-lecture13-contextual-representations.pdf [23.52M]
lecture 14 transformers and self-attention for generative models [7.73M]
suggested readings [2.02M]
image transformer.pdf [1.06M]
music transformer generating music with long-term structure.pdf [985.26K]
attention is all you need.pdf [2.10M]
cs224n-2019-lecture14-transformers.pdf [3.62M]
lecture 15 natural language generation [29.51M]
cs224n-2019-lecture15-nlg.pdf [29.51M]
lecture 16 reference in language and coreference resolution [20.30M]
cs224n-2019-lecture16-coref.pdf [15.90M]
cs224n-2019-lecture17-multitask.pdf [4.40M]
lecture 18 constituency parsing and tree recursive neural networks [20.56M]
suggested readings [564.98K]
parsing with compositional vector grammars.pdf [564.98K]
constituency parsing with a self-attentive encoder.pdf [458.94K]
cs224n-2019-lecture18-treernns.pdf [19.56M]
lecture 19 safety, bias, and fairness [11.41M]
cs224n-2019-lecture19-bias.pdf [11.41M]
lecture 20 future of nlp + deep learning [25.75M]
cs224n-2019-lecture20-future 加，.pdf [25.75M]
比赛 [6.48G]
kaggle文本分类比赛 [6.23G]
数据集 [6.08G]
embeddings.zip [5.96G]
sample_submission.csv [1.24M]
test.csv [4.99M]
train.csv [118.45M]
1.比赛介绍.wmv [19.89M]
2.数据分析.wmv [12.62M]
3.baseline模型(1).wmv [34.97M]
4.baseline模型(2).wmv [33.35M]
5.提交数据+提分策略.wmv [28.95M]
kaggle比赛介绍.pdf [2.99M]
kaggle比赛介绍.pptx [17.59M]
01零基础1小时完成一场ai比赛.pptx [17.88M]
02 达观杯文本智能挑战赛(入门指导).mp4 [95.76M]
02零基础1小时完成一场ai比赛.pptx [17.79M]
03达观杯之文本分类任务解析与代码使用(进阶指导).mp4 [111.39M]
03达观杯之文本分类任务解析与代码使用.pptx [16.51M]
python基础训练营（完结） [1.92G]
1.第一章绪论和环境配置.mp4.mp4 [56.30M]
10.【作业讲解】第五章：程序控制结构..mp4 [34.66M]
11.第六章函数-面向过程的编程..mp4 [129.58M]
12.【作业讲解】第六章：函数..mp4 [59.97M]
13.第七章类-面向对象的编程..mp4 [40.58M]
14.【作业讲解】第七章：类..mp4 [40.58M]
15.第八章文件、异常和模块..mp4 [131.24M]
16.【作业讲解】第八章：文件、异常和模块.mp4.mp4 [13.59M]
17.第九章有益的探索.mp4 [134.18M]
18.第十章python标准库.mp4.mp4 [96.20M]
19.第十一章numpy库.mp4 [90.57M]
2.【作业讲解】第一章：助教实际演示配置环境过程.mp4.mp4 [47.30M]
20.第十二章pandas库.mp4 [174.42M]
21.第十三章matplotlib.mp4 [128.25M]
22.第十四章sklearn库.mp4 [66.70M]
23.第十五章再谈编程.mp4 [74.78M]
3.第二章python基本语法元素.mp4.mp4 [127.54M]
4.【作业讲解】第二章：python基本语法元素.mp4.mp4 [80.63M]
5.第三章基本数据类型.mp4.mp4 [87.49M]
6.【作业讲解】第三章：基本数据类型.mp4.mp4 [79.13M]
7.第四章组合数据类型.mp4 [96.37M]
8.【作业讲解】第四章：复杂数据类型.mp4.mp4 [96.99M]
9.第五章程序控制结构.mp4 [82.76M]
pytorch框架第二期 [3.30G]
pytorch第二周作业讲解..ts [136.25M]
pytorch第一周作业讲解（1)..ts [59.89M]
pytorch第一周作业讲解（2)..ts [49.03M]
pytorch第一周作业讲解（3)..ts [47.22M]
第二周..txt [3.33K]
第二周第二节课：transforms与normalize..ts [86.89M]
第二周第三节课：transforms..ts [210.65M]
第二周第四节课：transforms（二）..ts [210.70M]
第二周第一节课：dataloader与dataset..ts [94.47M]
第六周..txt [0.72K]
第六周第二节正则化之dropout.ts [90.65M]
第六周第一节.ts [88.89M]
第三周.txt [3.29K]
第三周第二节课：模型容器与alexnet构建.ts [115.83M]
第三周第三节课.ts [119.52M]
第三周第四节课.ts [88.57M]
第三周第一节课：模型创建步骤与nn.module.ts [102.26M]
第四周…txt [3.62K]
第四周第二节课.ts [156.78M]
第四周第三节.ts [159.81M]
第四周第四节：优化器(一).ts [96.47M]
第四周第五节.ts [110.99M]
第四周第一节课：权值初始化.ts [97.34M]
第五周…txt [2.80K]
第五周第二节：tensorboard简介与安装.ts [67.14M]
第五周第三节.ts [125.89M]
第五周第四节.ts [180.19M]
第五周第五节.ts [140.87M]
第五周第一节.ts [139.43M]
第一周.txt [2.57K]
第一周第二节：张量简介与创建.ts [70.90M]
第一周第三节：张量操作与线性回归.ts [92.19M]
第一周第四节：计算图与动态图机制.ts [57.66M]
第一周第五节：autograd与逻辑回归.ts [96.93M]
第一周第一节：pytorch简介与安装.ts [109.11M]
开营仪式回放-老师部分.ts [178.95M]
贪心nlp (全-无密) [32.13G]
101-150.rar [3.82G]
1-50.rar [4.13G]
151-200.rar [3.66G]
201-250.rar [3.43G]
251-300.rar [4.26G]
301-350.rar [4.47G]
351-407.rar [6.17G]
51-100.rar [2.08G]
资料.rar [117.02M]
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