【备战秋招】面试刷题+算法强化训练营第三期（完结）

资料 [17.85K]

第二周：学习支持向量机.txt [3.50K]

第三周：了解机器学习中的非监督学习算法.txt [3.62K]

第四周：了解优化算法的原理.txt [3.04K]

第五周：学习前向神经.txt [3.90K]

第一周：了解机器学习中的特征工程和模型评估.txt [3.79K]

【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. 随机梯度下降和小批量随机梯度下降.ts [15.71M]

3.mcmc采样.ts [56.54M]

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.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——老师部分买课程.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.ts [30.23M]

硬间隔svm最优化问题的推导.ts [108.36M]