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DSMM Advantage: Use DNN models to do this. Key word: DNN Usage: Used in text-based query-to-document retrieval. Objective Retrieve documents related with an input query. Proposed DNN architecture: Term vector Term vector use letter or word as a dimension and counts of that term. Word hashing In order to reduce the dimensionality of bag...

Cross-entropy loss Usage: Optimize classifier. Main idea: It measures the distance between predicted output probability distribution and actual probability distribution. https://gombru.github.io/2018/05/23/cross_entropy_loss/

unigram distribution Key word: Evaluation, Loss Function Usage: non-contextual probability of finding a specific word form in a corpus.

GBDT Advantage: The advantage of GBDT is that it achieve great performance. 对数据特征尺度不敏感，自动填补确实特征，可做特征筛选，效果较为突出。 Key word: Regressor Usage: A regressor to forecast different labels or values Great Explanation GBDT Algorithms: Principles - Develop Paper Watch chapter 3 for formulation Adobe Acrobat Core idea Whether weak learners could be m...

ARIMA Key word: Model, Time Series Usage: A statistical analysis model that uses time series data to either better understand the data set or to better predict future trends. Background Stationary and differencing Stationary time series does not depend on time at which the series is observed. Stationary time series will not have any predict...

Term-vector It means that each word forms a separate dimension: For a model containing only three words you would get: dict = { dog, cat, lion } Document 1 “cat cat” → (0,2,0) Document 2 “cat cat cat” → (0,3,0) Document 3 “lion cat” → (0,1,1) Document 4 “cat lion” → (0,1,1)

Softmax Advantage: Map the original output vector to a space of [0,1] Key word: Activation Function Usage: Normalize the output of a network to a probability distribution over predicted output classes. Used in multiclass classification problems. Form \[\begin{equation} P(y=j|x) = \frac{e^{x^Tw_j}}{\sum^K_{k=1}e^{x^Tw_k}} \end{equation}\]...

Recommendation Algorithms Advantage: Efficient and effective. Usage: Recommendation Systems Collaborative Filtering User-based filtering 向用户推荐与他相似的用户感兴趣的物品。 基本步骤： 计算用户之间的相似度 根据相似度对用户进行评分 找到相似用户集合中其他用户感兴趣但是从未出现在该用户列表中的物品，推荐给该用户 相似度计算方法 余弦相似度，Cosine similarity 给定两个向量$A,B$，余弦相似性有点积和向量长度给出 \[\begin{equation} \...