When I was preparing for the autumn move , Every time I see those great gods holding seven or eight big factories on Niuke online offer, I can't envy it , At that time, my level of machine learning algorithm could not be put on the table , At most, I read several courses 、 Did two game levels , Then the game hasn't got any ranking , I really began to worry so much , Many small friends around have started internships in big factories , And I really ask myself , Almost zero internship experience , The algorithm has not been systematically deduced ,leetcode Only occasionally brush , About twenty questions have been brushed , It's not too much to describe the state at that time with disaster , I'm starting to panic , Then I began to calm down and find all kinds of experience posts , Niuke is a gathering of big men , I've read about ten articles here , I've read several articles on , I summed up several key investigation directions of autumn recruitment algorithm post ： Self introduction. 、 Project introduction 、 Algorithm details 、 Data structure and algorithm , Next, let me talk about the details of these four directions one by one .

Self introduction.

Good introduction , It will leave a deep impression on the interviewer , And this piece is all worded by yourself , The key point to highlight is to control yourself , So from your narrative , The interviewer can hear your familiarity with the project and your depth of thinking , therefore , It is particularly important to prepare in advance , During the interview, each project should be described according to a certain logic , Of course, the most important thing in the algorithm project is the data 、 features 、 Model 、 effect , According to this framework, I made it clear , The interviewer listened with ease , The next interview stage will also be more fluent , Because the interviewer will capture the key words in your self introduction , I'll ask you a series of questions in the Q & a session later , This implies a tip, That's what you said , Be sure to understand better than the interviewer , Don't move in those vague things , Or sooner or later it will show up .

Project introduction

That's the top priority , The project will reflect the overall quality of an interviewer . What should we do if we have no internship experience ？ Easy to handle , Go to the game , Try to get the place in the competition , Then make the algorithms used in the game familiar , The interviewer is bound to expand on the details of your project , Let you dissect some of his doubts , For example, the selection of positive and negative samples 、 Feature handling 、 Details of the model , Another example is that you used a tree model in your game , You have to know all the knowledge points related to the tree model , Let me just give you a few examples ：XGBoost Why not be sensitive to missing values ？ Compared with ordinary GBDT,XGBoost How to deal with missing values ？ Why? xgboost/gbdt Why the depth of the tree is rarely able to achieve high accuracy when adjusting parameters ？ Wait, this kind of detailed question , Once you answer vaguely , The interviewer is bound to deduct points , So don't take chances , I don't think the interviewer will ask , Murphy's theorem tells us , Anything that can go wrong is bound to happen , These can be prepared , It's almost an open book exam , Why not think about how to answer in advance , I have to go to the examination room and rack my brains to think of a different answer . If the competition platform , You must know , Like Ali Tianchi 、kaggle And so on are all platforms for everyone to participate in the competition .

Algorithm details

In addition to examining the details of the algorithms that appear in the project , You will also ask questions about the basis of your machine learning algorithm , Let me summarize some of the more important , Traditional algorithms : Logical regression 、 Naive Bayes 、 Tree model （random forest/Adaboost/xgboost/lightgbm）、SVM、PageRank、 clustering ; Some theories of machine learning , Nonequilibrium problem 、 Over fitting problem , Cross validation problem , Model selection ; Recommendation system : Collaborative filtering 、FM/FFM、LS-PLM、Wide&Deep、DeepFM、DIN、DIEN、ESMM、Embedding、 Recall 、EE、 Performance evaluation ; These are the core of the algorithm post , Besides , Investigation of some code languages , It will also be something that some interviewers value very much , such as C++/python/Spark etc. , In order to prepare for this , I turned all the questions I could think of into questions and answers , Prepare yourself by asking and answering questions , I listed all the questions at the end of the article . You can prepare according to this , Or choose some of them . Because my job is to recommend algorithms , Therefore, it will pay more attention to this aspect , Later, if I have time , It can be extended to other fields , Like natural language processing 、 Computer vision and so on . Recommended books , It is also some experience of Niuke's great God , expericnce 《 Statistical learning method 》、《 Baimian machine learning 》、《 In depth learning 》、《 Deep learning recommendation system 》、 Zhou's 《 machine learning 》. Of course, reading these books is definitely not enough , Reading it doesn't mean you have mastered , You follow the list of questions I made for you , Answer it in your heart , Or write it directly , This effect is absolutely top , When I recruit in autumn , It's almost a second , In your own hands . I will also issue these questions in my official account , Basically finished , You can also see my website , Website 、 The official account is introduced below , Welcome to exchange .

Data structure and algorithm

This is also quite critical , Some companies will even decide your future according to your performance in this field , Like the headline is the famous master of dynamic programming , I always like to take some intermediate or hard The topic , It's a headache . Many non computer students have no foundation , Practice is not in place , You may not think clearly on the spot . My suggestion is to brush according to the topic first , Like dynamic programming 、 Sliding window theme 、 Double pointer 、 Speed pointer 、topK wait , Brush up 200 The question is about , Then you can brush at random , Be sure to brush more questions , This is an interview rule that cannot be overemphasized a hundred times . The recommended books are 《 The finger of the sword offer》, Website, you can find leetcode Chinese net .
Okay, talk less , The best time to plant a tree is ten years ago , The second is now , If you think your algorithmic skills are not enough, you can gain in the process of autumn moves offer Words , Then from now on, conquer one by one , It's not hard . I will also update my algorithm and related questions and answers on my website and official account 、 The interview experience 、 Push and so on , If you are interested, you can pay attention to a wave , My expectation for this number is to do it for a long time , I will also do it with my heart , I hope it can bring real help to the students of the algorithm .

Website ：http://ml-union.cn
official account ： Opiate algorithm
Qr code for public account ： as follows

file

Here are the questions I sorted out above , I will keep updating this list , The main writing area in the later period is in the official account , It is estimated that Niuke will not come often , Welcome to my wechat , Communicate together .

Personal wechat ：ayao-algo

Machine learning is full of questions and answers

Logical regression

Let's deduce the loss function of logistic regression , And explain what it means .
In the advertisement LR In the model , Why do feature combinations ？
Why? LR The model uses sigmoid function , What is the mathematical principle behind ？ Why not use other functions ？
Why? LR It can be used to predict the click through rate ？
Data meeting what conditions are used LR best ？ let me put it another way , in order to LR Work better , What to do with the data ？
Can logistic regression solve the nonlinear classification problem ？
To one with m Samples n Data sets of dimensional features ,LR What is the dimension of the gradient in the algorithm ?
Why does the logistic regression loss function use the maximum likelihood estimation instead of the least square method ？
How to solve the parameters of logistic regression ？
SVM and LR What are the similarities and differences ？ Under what circumstances ？
Why? LR res MSE？
Why does logistic regression need to discretize the features first ？
parallel LR The implementation of the
Logical regression （Logistic regression） What are the applications in the financial field ？

Naive Bayes

What is Bayesian decision theory ？
Do you know what naive Bayes is ？
There are men in the company 60 people , Women have 40 people , The number of men wearing leather shoes is 25 people , The number of people wearing sneakers is 35 people , The number of women wearing leather shoes is 10 people , The number of people wearing high heels is 30 people . Now all you know is that one person is wearing leather shoes , Then you need to guess what his gender is . If you infer that he is a man, the probability is greater than that of a woman , Then think he's male , Otherwise I think he's a woman .
Can you tell me about the advantages and disadvantages of naive Bayes ？
“ simple ” This is the disadvantage of naive Bayes in prediction , So there is such an obvious assumption, the disadvantage is , Why can naive Bayesian prediction still achieve good results ？
What is Laplace smoothing ？
Is there any super parameter in naive Bayes that can be adjusted ？
How many models are there in naive Bayes ？
Do you know what applications naive Bayes has ？
Is naive Bayes a high square error or low variance model ？
What are the assumptions of naive Bayes ？ What are the advantages and disadvantages ？
How naive Bayes estimates parameters ？
What's the difference between Bayesian school and frequency school ？
What is the difference between logistic regression and naive Bayes ？

Tree model

Talk about your understanding of entropy 、 Understanding of information gain and information gain ratio ？
ID3 What is the division standard of the algorithm ？
ID3 What's wrong with the algorithm ？C4.5 How does the algorithm solve ID3 Defective ？ID3 and C4.5 What's the flaw ？
C4.5 How to deal with missing values ？
C4.5 What are the criteria for the division of ？
C4.5 What are the shortcomings of the algorithm ？
What is the definition of Gini coefficient and its advantages ？
CART How to select partition features when eigenvalues are missing ？
Select the division feature ,CART How should the model deal with the samples missing the eigenvalue ？
Reasons for over fitting of decision tree and its solutions ？
What are the strategies for decision tree pruning ？ What are their advantages and disadvantages ？
C4.5 What is the pruning method used ？
CART How to deal with category imbalance ？
CART How to deal with continuous values ？
Please tell me ID3、C4.5 and CART The difference between the three .
CART Why did the algorithm choose gini Index ？
C4.5 How does the algorithm deal with continuous values ？
How does the decision tree deal with missing values ？
How to calculate the importance of each feature of the decision tree ？
If there are many features , Must the last unused feature in the decision tree be useless ？
Does the decision tree need normalization ？
Since the use of neural network can also solve the classification problem , that SVM、 What is the significance of these algorithms in decision tree ？
Relationship between decision tree and conditional probability distribution ？
CART What is your pruning strategy ？
If it is caused by outliers or uneven data distribution , What impact will it have on the decision tree ？
What are the advantages of decision tree compared with other models ？
The difference between decision tree and logistic regression ？
What is the difference between classification tree and regression tree ？
How to understand the loss function of decision tree ?
sklearn Whether the decision tree in should be used one-hot code ？
Briefly describe the steps of random forest
Whether the random forest will appear ？
Why not divide random forest into training set and test set ？
How random forests deal with missing values ？
Random forest and GBDT The difference between
Random forest and SVM Comparison
Talk about the advantages and disadvantages of random forest
Briefly Adaboost The weight update method of
Let's deduce Adaboost Sample weight update formula
During training , Why is there always the problem of wrong classification in each round of training , Whole Adaboost But it can converge quickly ？
Adaboost Advantages and disadvantages ？
AdaBoost And GBDT What are the similarities and differences of comparison ？
Please give us a brief introduction GBDT Principle
Why can regression trees be used as GBDT Iterative learning machine ？
GBDT How it is used to classify problems ？
Why? GBDT take CART The regression tree is divided into m A binary tree （ Each tree has only two leaf nodes ）, Instead of asking for a tree m+1 A binary tree of layers （ At most 2m A leaf node ）？
GBDT How to regularize ？
gbdt Why do we use negative gradients instead of ？
GBDT What are the advantages of ？
GBDT What is the role of reduction in ？
Why based on residuals GBDT It's not a good choice ？
Why is it said in the gradient lifting tree that the negative gradient of the objective function with respect to the current model is the approximate value of the residual ？
Why? xgboost/gbdt Why the depth of the tree is rarely able to achieve high accuracy when adjusting parameters ？
Why in the actual kaggle In the game ,GBDT and Random Forest The effect is very good ？
GBDT How to use it in click through rate prediction ？
GBDT How to calculate the gradient in ？ The gradient of who to whom ？
m×n Data sets , If you use GBDT, So how many dimensions is the gradient ？ Or it's related to the depth of the tree ？ Or it is related to the number of leaf nodes of the tree ？
Random forest and GBDT The differences and similarities
In machine learning algorithm GBDT And Adaboost What are the differences and connections between ？
Introduce to you XGBoost Principle
XGBoost And GBDT What's the difference
RF and GBDT The differences and similarities
XGBoost Why use Taylor's second-order expansion
XGBoost How to implement the parallelization part of ?
XGBoost Why fast ？
XGBoost How to calculate the weight of leaf nodes in the middle ？ Why can leaf node score be used to measure the complexity of the tree ？
XGBoost Stop growing condition of a tree in
Please deduce Xgboost
XGBoost What are the methods to prevent over fitting ？
XGBoost How to deal with unbalanced data
Compare LR and GBDT, Tell me what situation GBDT Not as good as LR
XGBoost How to prune trees in
Use XGBoost Training model , If it is fitted, how to adjust the parameters ？
XGBoost How to choose the best splitting point ？
XGBoost Of Scalable How sex is reflected
XGBoost How to evaluate the importance of features
XGBooost General steps of parameter tuning
XGBoost If the model is over fitted, how to solve it
XGBoost Why not be sensitive to missing values ？ Compared with ordinary GBDT,XGBoost How to deal with missing values ？
XGBoost How to realize the regularization of ?
XGBoost and LightGBM The difference between
XGBoost How to ask Hessian The inverse of a matrix ？
xgboost In the algorithm, the approximate algorithm is used to obtain the segmentation points ？
LightGBM Compare with XGBoost What are the advantages and disadvantages ？
Please introduce several common integrated learning frameworks ：boosting/bagging/stacking
Why integrated learning is better than a single learner ？
Please briefly describe the meaning of variance and deviation of the model ？
Must the base model in ensemble learning be a weak model ？
Please calculate the overall expectation and overall variance of the model
Why? Bagging The base model in must be a strong model ？
Why? Boosting The base model in the framework must be weak ？

Feature Engineering

Machine learning , What are the engineering methods for feature selection ？
In the ad click through rate model ,LR, GBDT+LR, FM, DNN Advantages and disadvantages of such model ？ What's the actual effect ?
Multi label （multi-label） Data learning problem , What are the commonly used classifiers or classification strategies ？

SVM

About SVM in , Yes, constant C The understanding of the ？
machine learning SVM About why the function interval can be set to 1？
Machine learning has a lot to say about kernel functions , What is the definition and function of kernel function ？

optimization algorithm

optimization algorithm
What is gradient descent method ？
Training with gradient descent SVM What's the problem ？
least square 、 Maximum likelihood 、 What's the difference between gradient descent ？
In optimization problems , Why does Newton method need fewer iterations than gradient descent method ？
Why? nn The bigger problem is that it will fall into local optimization , The convex function is not selected as the activation function ？

Loss function

Please explain the definition of loss function
Please talk about your understanding of logistic regression loss function
Please talk about your understanding of the square loss function .
Please talk about your understanding of exponential loss function .
Please tell me about you Hinge Understanding of hinge loss function .
Please return to logic and SVM Compare the loss function of .
For logical regression , Why is the square loss function nonconvex ？
How to make SVM The derivation of is related to the loss function ？
How neural networks design their own loss function, If you need to modify or design your own loss, What rules to follow ？
softmax and cross-entropy What's the relationship ？
Why is the loss function of neural network non convex ?
What loss functions are commonly used in deep learning ( Optimize the objective function )？
Neural network , Design loss function What are the techniques ?
Neural network , Why not take the partial derivative of the loss function and make it equal to zero , Find the optimal weight , Instead, use the gradient descent method （ iteration ） Calculate weight ？
When using the cross entropy loss function , I just want to punish 0.4~0.6 Such a fuzzy value , How to change ？

Regularization

Please explain the meaning of regularization .
What is the relationship between regularization and a priori distribution of data ？
L1 Compared with L2 Why is it easy to get sparse relief ？
L1 Why can regularization make the coefficient become 0？L1 How to deal with 0 Point non differentiable case ？
Deep learning how to prevent over fitting ？
Multiple... Are used simultaneously in the objective function L1 and L2 The case of regularization term , How to solve ？

AUC

Please explain AUC.
AUC And accuracy must be positively correlated ？ Is there any internal relationship ？
Accuracy 、 Recall rate 、F1 value 、ROC、AUC What are the advantages and disadvantages of each ？
Why? accuracy、precision、f1-score、recall They all scored high, but AUC Low score ？
Machine learning ,F1 and ROC/AUC, How to do index evaluation for multi classification ？
How to solve offline and online problems auc Inconsistent with the online click through rate ？
Why? AUC Insensitive to the proportion of positive and negative samples
AUC How much does it take ？
AUC A probabilistic explanation for .

Unbalanced data

What are the processing methods of unbalanced data sets in machine learning ？
Please give us a brief introduction SMOTE How does the sampling method deal with unbalanced data ？
The original SMOTE What's wrong with the algorithm ？ How to improve ？
Please give us a brief introduction Tomek Links Under sampling method .
Please give us a brief introduction NearMiss Method
EasyEnsemble How does the algorithm solve the problem of unbalanced data ？
BalanceCascade How does the algorithm solve the problem of unbalanced data ？
SMOTE Oversampling and Tomek Links Can undersampling algorithms be combined ？

The recommendation system is full of questions and answers

Shallow model

Please briefly describe user based collaborative filtering UserCF The recommendation process .
In user based collaborative filtering , How to calculate the similarity of users ？
User based collaborative filtering UserCF What are the shortcomings ？
Please briefly describe the article based collaborative filtering ItemCF The recommendation process .
Please briefly describe the off-line engineering implementation of item based collaborative filtering algorithm （spark）
The large-scale sparse matrix multiplication in the calculation process of collaborative filtering algorithm spark Existing solutions in （multiply） What are the drawbacks ？ How to improve ？
Please briefly describe the advantages and disadvantages of collaborative filtering algorithm
In real business , In order to make collaborative filtering more valuable to the business , What problems should we pay attention to when using this algorithm ？
Collaborative filtering algorithm will have the problem of cold start , Mainly reflected in ？
What recommended business scenarios can collaborative filtering algorithms be used for ？
Please briefly describe the engineering implementation of near real-time collaborative filtering algorithm
What is the principle of matrix decomposition ？ What are the main methods to solve ？
How to understand the matrix decomposition model from the perspective of deep learning model
Matrix decomposition algorithm , The length of the hidden vector k How does the value of affect the effect and project cost ？
Please briefly describe the process of singular value decomposition . What are the defects of singular value decomposition ？ Why not apply to the solution in the Internet scenario ？
Please briefly describe the gradient descent method to solve user - The process of implicit vector of goods .
How to solve the problem of scoring deviation between users and items in matrix decomposition ？
The logistic regression model predicts that compared with collaborative filtering , What is the biggest advantage ？ What is the recommendation process ？
Please deduce the mathematical form of logistic regression .
Please deduce the process of solving the parameter update of logistic regression by gradient descent method .
Logistic regression as CTR What are the advantages and disadvantages of the prediction model ？
In industry , Very few use continuous values as LR The feature input of the model , Instead, the continuous features are discretized into a series of 0、1 features , What are the advantages ？
CTR All features in the estimate are crossed in pairs , What are the shortcomings of the method of giving weight to all combinations ？
FM What is the principle of ？ What is the connection with matrix decomposition ？
FM Compare with POLY2 Why is generalization better ？ What are the advantages in Engineering ？
FFM Compare with FM What's the improvement ？
FM What is the training complexity of ？ How to deduce ？FFM What is the training complexity of ？
Why? GBDT It can be used for feature selection and feature combination ？
GBDT+LR In the composite model ,GBDT How to generate eigenvectors ？
GBDT+LR What are the advantages and disadvantages ？
Please briefly describe what Ali's mother put forward LS-PLM The principle and mathematical form of the model .
LS-PLM What are the advantages of the model ？
LS-PLM What is the relationship between the model and the deep learning model ？
Depth model
Please briefly Deep Crossing Network structure .
Deep Crossing What is the role of the residual element in ？
NeuralCF Based on the matrix decomposition model , What improvements have been made ？
PNN Comparison NeuralCF and Deep Crossing What are the improvements ？ What are the advantages ？
PNN What are the ways of feature intersection in ？ What's the difference ？
PNN What are the advantages and limitations of the model ？
How to understand Wide&Deep Model Memorization？
Wide&Deep Medium Memorization What are the disadvantages ？
How to understand Wide&Deep Model Generalization？
Wide&Deep Medium Generalization What are the disadvantages ？
Please briefly Wide&Deep Model structure of .
Wide&Deep In training, we used Joint Training, What are the benefits ？
wide&deep How does the author apply wide&deep To make recommendations ？
Why? wide&deep For model ftrl and adagrad Two optimization methods ？
Wide&Deep What are the innovations and advantages of the model ？
In the application scenario , Which features are suitable for Wide Side , Which features are suitable for Deep Side , Why? ？
wide&deep Why should continuous features be discretized in the model ？
Deep&Cross comparison Wide&Deep What improvements have been made ？Deep&Cross Model Cross How does the network operate ？
FNN What is the main purpose of the model to solve ？ How it was solved ？
FNN What is the model structure of ？
FNN How to use... In the model FM To initialize the Embeddiing Of layer parameters ？
DeepFM What is the motivation for the proposal ？
DeepFM in FM Layer and NN Layers are shared features Embedding What are the benefits of ？
DeepFM Compare with Wide&Deep What's the improvement ？ Why is it so improved ？
NFM comparison Wide&Deep What's the improvement ？ Why is it so changed ？
What are the characteristics of users' interest in goods ？DIN How to capture these characteristics of user interest ？
Capture of user interests , What methods do we usually have ？
DIN How to handle the input of ？
DIN How is the activation unit designed ？
Why add cross product as input ？
Why use simple MLP Realization AU Well ？
DIN The activation function is used in Dice Replace the classic PReLU Activation function , What are the advantages ？
DIN An adaptive regularization algorithm is used , What is its motivation ？
DIN Used in the paper GAUC As an evaluation indicator , What are its benefits ？
DIEN What is the motivation for introducing sequence information ？
Please draw DIEN The structure of each layer ？ Interest extraction layer and interest evolution layer .
CVR Estimate what to estimate ?
And CTR Different estimates ,cvr What are the data sparsity and sample selection bias faced in the estimation ？
ESMM How to solve the problem of sample selection deviation ？
ESMM How to solve the problem of data sparsity ？
ESMM The structure of is based on “ ride ” The relationship design is not based on what is the reason for division ?
Explain it. ESMM The objective function of

Embedding

Embedding Why is technology important for deep learning recommendation systems ？
Please briefly Word2Vec Principle and structure of .
In order to speed up Word2Vec Training for , What methods have been taken ？
Talk to you about Item2Vec The understanding of the , What are its limitations ？
Please briefly describe the structure of the two tower model , What is the function of the item tower ？
The more complex the model structure, the better ？ The more features, the better ？
DeepWalk What is your main idea ？ Say the algorithm steps
Node2Vec What does homogeneity and structure mean in ？ They and DFS and BFS What is the corresponding relationship between ？
Please write out Node2Vec Jump probability formula between nodes .
Illustrate with examples Node2Vec The intuitive explanation of homogeneity and structure in Recommendation System .
EGES The proposal of is mainly to make up for DeepWalk Defective , How to make up for it ？
Please briefly EGES Structure of model , And briefly describe the practice of each layer .
Embedding What are the applications in the deep learning recommendation system ？ List three directions .
Embedding What are the problems with training as a deep learning model ？
Embedding What are the pre training methods , Separately .
Please briefly Embedding As a process of recall layer .
Please briefly describe the principle of local sensitive hash and its role in Recommendation System .

Recall

Suppose the number of inventory reaches the level of millions , How to design methods to recommend to users from this level of quantity top10 The items , At the same time, it can reduce the pressure of calculation ？
Why sorting is more concerned than recall ？
What are the characteristics of the recall model that are significantly different from the ranking model ？
Why not just take " The exposure did not click " Make a negative sample of the recall model ？
How does the recall model randomly sample negative samples ？
What are the drawbacks of using random samples as negative samples ？ How to solve ？
Why is there a recall in the recommendation system ？ What are the similarities and differences between recall and ranking in the recommendation system ？
Recommend how system recall is implemented item The pressure of ?
CTR What are the goals of the prediction and recommendation system gap？
Reality recommendation system only by " forecast CTR" Sort ？
Why? CTR Estimates apply only to those with “ True negative ” Sample scenario ？
Which scenarios in the recommendation system cannot obtain true and negative samples ？ How to solve ？
Please briefly describe based on embedding Recall method for , What are the advantages ？
Airbnb Recall algorithm listing embedding How to select positive and negative samples for recall ？
Airbnb Recall algorithm user/listing-type embedding How to select positive and negative samples for recall ？
Facebook Of EBR How the algorithm selects positive and negative samples ？
Why does the recall require isolation user And item Decoupling of features ？ How to decouple ？
In the recall scenario , Why often use Pairwise LearningToRank To build the relative accuracy of sorting ？
Optimize recall Pairwise LearningToRank What forms of loss function can be used ？
Pinterest Of PinSAGE How to build a positive sample ？
Please briefly DSSM The principle of the model
DSSM The input layer maps text into a low dimensional vector space and transforms it into a vector What's the problem ？
Please briefly describe DSSM How is it applied to recall ？ What is the structure ？
Please give us a brief introduction DSSM Advantages and disadvantages
DSSM Why is the negative sample in random sampling , without “ The exposure did not click ” When the negative sample ？
Please briefly describe Baidu's twin tower model
Please briefly YoutubeDNN The structure and principle of
Please briefly describe the user multi interest network MIND What is your starting point , Describe its structure ？
SDM How to combine the long-term and short-term interests of users ？ How is its structure ？

Feature Engineering

Feature engineering that allows you to design a recommendation system , How would you design ？ Including user side 、 Item side and contextual features .
During feature processing , How to deal with continuous features ？
During feature processing , How to deal with category features ？

Explore and use

When new users register or new items are put into storage , How to provide users with satisfactory recommendation services , And how to recommend new items , Recommend it to users who like it ？
Briefly describe the meaning of exploration and utilization .
Greedy What is the principle of the algorithm ？ What are the drawbacks ？
Please briefly Thompson Sampling Principle and steps of the method .
Excuse me, UCB How to solve the problem of exploration and utilization in cold start ？
sketch LinUCB The principle and specific practice of .

Characteristic evaluation

How does the real-time performance of the model affect the effect of the recommendation system ？
How does the client recommend real-time features in real time ？
How does the stream computing platform perform quasi real-time feature processing ？
Distributed storage system HDFS And the role of distributed batch processing platform in recommendation ？
Please briefly offline/nearline/online Training methods and steps .
Please name several indicators for offline evaluation .
Please say P-R curve 、ROC Curves and AUC The meaning and relationship of .
How to skillfully draw ROC curve ？
Offline AUC promote , Will it certainly lead to the improvement of online indicators ？ Why? ？
Why AB test ？ What are the advantages over offline evaluation ？
AB How does the test design the layering and shunting mechanism ？

Deep learning

Please write down the commonly used loss function , Loss of square 、 Cross entropy loss 、softmax Loss function and hinge Loss function .
Why is the training of deep neural network very difficult ？ What are the main reasons .
Please illustrate forward propagation and back propagation with examples
What is the function of introducing nonlinear activation function into deep learning ？
Please name the commonly used activation functions , And draw their corresponding images .
How to choose the activation function ？ Please describe the characteristics of various activation functions .
Relu What are the advantages of activation functions ？
Please explain Softmax Definition and function of activation function ？Softmax How the activation function applies to multiple classifications ？
In depth model training , Why batch size? How to choose the right one batch size, Have and influence on the results ？
Please explain BN Principle , Why batch normalization ？
What is model tuning fine tuning？ Please explain fine-tuning Three states of the model , What are the characteristics of each ？
Why unsupervised pre training can help deep learning ？
What are the methods for initializing weight deviation ？ Explain their characteristics .
What is the role of setting the learning rate ？ What are the common learning rate attenuation methods ？ Explain their respective characteristics
What are the methods to prevent over fitting in deep learning ？
Please name several common optimization algorithms , And their respective characteristics .
How to balance variance and deviation in deep learning ？ If the deviation is too large, what should we do ？ The variance is too large ？
Please explain Dropout Principle , During training and testing dropout What's the difference ？
Data enhancement methods commonly used in deep learning ？
How to understand Internal Covariate Shift？

C++ A hundred questions and answers

Basics

What is the role of variables ？ What is the syntax for creating variables ？
C++ What is the function of constants in ？ Please write down two ways to define constants .
Please give me a few C++ Examples of pre reserved keywords in
short type 、int type 、long The type and long long What is the memory space occupied by each type ？
sizeof What is the function of keywords ？
What is the memory space occupied by character variables ？ What are the characteristics of it in storage ？
Please give me some examples of C++ Escape characters in ？
C++ The difference between pre increment and post increment is ？
Write an example of a ternary operator ？ And explain .
switch case In the sentence break What is the role of ？
One for The starting expression in the loop statement 、 Conditional expression 、 What is the execution order of the end loop body and loop statements ？
break Statement and continue What is the function of the sentence ？

Array

What are the characteristics of arrays ？ How to define an array ？
What is the relationship between the name of a one-dimensional array and its memory address ？
How to define a two-dimensional array ？ What is the relationship between the name of a two-dimensional array and its memory address ？

function

Explain the meaning of form participation arguments .
What is the meaning of value passing ？ What are the effects on formal and arguments ？
What function declarations do ？

The pointer

What does the pointer do ？ What is the difference between pointer variables and ordinary variables ？
How much memory space does the pointer occupy ？
Constant pointer 、 What's the difference between pointer constants ？
What's the difference between value passing and address passing ？

Structure

How to create a structure ？ Please write down two methods .
How to create a structure array ？
Structure pointer how to access members of a structure ？
How structures nest structures ？ For example
Can a structure pass parameters to a function as parameters ？

Memory

Please briefly C++ When the program executes, each memory block （ Code section 、 Global area 、 The stack area 、 Heap area ） Functional characteristics of .
new What is the function of the operator ？ How do you use it? ？

quote

What is the function of reference ？ What is the essence of it ？
When a reference is used as a function parameter , And value passing 、 What's the difference between address passing ？
What are the functions and writing methods of constant references ？
When writing function default parameters , What do you need to pay attention to ？
heavy load
What conditions need to be met for function overloading ？

encapsulation

What is the meaning of encapsulation ？
What are the access permissions for the members and behaviors of the class ？ What's the difference ？
What's the difference between a class and a structure ？
What are the advantages of setting member properties private ？

Initialization and cleanup

What are the functions of constructors and destructors ？
What is the constructor Syntax ？ What are the characteristics of constructors ?
What is the destructor Syntax ？ What are the characteristics of destructors ?
What are the constructor call rules ？
Please explain C++ Deep copy and shallow copy in ？
C++ What is the syntax for initializing the list in ？
B There are objects in the class A As a member ,A For object members , When creating a B Object time ,A And B The order of construction and deconstruction is who comes first and who comes second ？
What are the characteristics of static members ？
Are member variables and member functions stored separately in a class ？ Do non static member variables occupy object space ？
this What does the pointer do ？
const What effect does decorating member functions have ？ keyword mutable What is the role of ？
C++ What is the role of Chinese friends ？ Global function 、 class 、 How are member functions implemented as friends ？
What are the ways of inheritance ？ What is its authority ？
Can a subclass inherit the private members of the parent class ？
What is the constructor and destructor order of parent and child classes ？
When a member with the same name appears between a subclass and a parent class , How to use subclasses , Access to data with the same name in a child or parent class ？
What problems does diamond inheritance bring ？C++ How to solve it in ？
What is the difference between static polymorphism and dynamic polymorphism ？
What are the satisfaction and use conditions of polymorphism ？
What are the advantages of polymorphism ？
What is the meaning of pure virtual functions ？ What is grammar like ？ What does it have to do with abstract classes ？
Explain the meaning of virtual destruct and pure virtual destruct 、 Grammar and its differences ？
How to create a function template ？ What does it do ？ What to pay attention to ？
What is the difference between a normal function and a function template ？ What are the calling rules ？
What is the purpose of materializing the function template to solve ？
What is the role of class templates ？ What is grammar like ？ What's the difference with function templates ？
When to create a member function in a class template
Please explain STL In the container 、 Algorithms and iterators .

python A hundred questions and answers

python in list、tuple、dict、set What's the difference between other types ?
What are the forms of function arguments ？ What are the characteristics of each ？
Please explain python Default parameter trap problem .
Please give an example to illustrate the difference between shallow copy and deep copy
What are the concepts of generator and iterator ？
Please briefly describe the built-in functions zip Usage of . When the length of iterators is inconsistent , How is it handled , Is there any alternative ？
Higher order function map/reduce/filter/sorted What are the usages of ？ Illustrate with examples .
What is the concept of closure ？ Illustrate with examples .
What are the benefits of anonymous functions ？ Please give an example to illustrate its usage .
What is the concept of decorator ？ How to use ？
What is the concept of partial function ？ How to use ？
enumerate comparison range What are the advantages ？
What is a factory function ？ Illustrate with examples .
Illustrate the difference between class attribute and instance attribute .
Please explain the concepts of inheritance and polymorphism with examples .
How to set access restrictions on attributes in a class ？
How to use __slots__？
Custom class __str__,__iter__, __getitem__,__getattr__,__call__ What's the use of separation ？
Static methods 、 What is the difference between class methods and member methods
@classmethod, @staticmethod, @property What are these ？
__init__ and __new__ What's the difference ？
What is? Python introspection ？
python How to manage memory ？
What is? GIL？
Please briefly python Exception handling mechanism of .
How do you position python programmatic bug Of ？ stay python How to realize single step execution in ？
assert What's the use of assertions ？
What are the built-in properties of the class ？
How can a list of elements that are strings be transformed into a space delimited string ？
python Medium is How operators compare ？
Please write a regular expression that matches the email address .
python How to pass command line parameters ？
How to understand python Thread in ？
Please briefly python Multiple processes in .

Spark A hundred questions and answers

Please briefly RDD The concept of , How to create RDD？
RDD What operations are supported ？
RDD What operations are supported ？ What are the characteristics of each ？
Please give an example of RDD Transformation operation and action operation
explain RDD Inert evaluation mechanism
Explain the conversion operation respectively map、flatMap、filter、distinct Function and usage of
Explain the conversion operation respectively union、intersection、subtract、cartesian Function and usage of
Explain the action and operation respectively reduce、fold、aggregate The usage and difference of
Why is it right RDD persist , Please briefly describe the different levels of caching mechanisms .
Please briefly reduceByKey、groupByKey、combineByKey The role and difference of .
RDD The meaning and difference between wide dependence and narrow dependence
RDD in Client、Master、Worker、Driver、Executor What are the meanings of
DataFrame And RDD The main difference is ?
Why? Spark Faster than mapreduce？
Spark There are those components ？
Spark Streaming The basic principle of
How to solve Spark The problem of data skew in
Please explain SparkSQL Three species join 1.Broadcast Join 2.Shuffle Hash Join 3.Sort Merge Join Meaning and difference
spark Can replace hadoop Do you ？
Executor The role of ？
Driver The role of ？
spark Rdd The cache of ？
spark spark-submit What are the parameters of the script ？
Wide dependence and narrow dependence
summary Rdd operator （30 More than ）
coalesce and repartition The difference between ？
reduceByKey and groupByKey The difference between ？
union and intersection The difference between ？
What are the commonly used wide dependency operators and narrow dependency operators ？
DAG How to divide stage？
How to divide job？
Spark The choice of persistence ？
Application scenarios of persistence and fault tolerance ？
What is an accumulator ？
What are broadcast variables ？
Nodes and task The relationship of execution ？
cluster Mode how to view logs
Spark Optimize ？
What is? DataFrame？
Rdd,DataFrame,DataSet The difference between ？
spark Bottom core RDD Cache mechanism 、 Application scenarios 、 How to use 、 How to clear the cache
DAG Directed acyclic graphs and partitions stage

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All questions and answers for algorithm posts of super large factories (recommended system / machine learning / deep learning / C + + / spark / Python)

Machine learning is full of questions and answers

Logical regression

Naive Bayes

Tree model

Feature Engineering

SVM

optimization algorithm

Loss function

Regularization

AUC

Unbalanced data

The recommendation system is full of questions and answers

Shallow model

Depth model

Embedding

Recall

Feature Engineering

Explore and use

Characteristic evaluation

Deep learning

C++ A hundred questions and answers

Basics

Array

function

The pointer

Structure

Memory

quote

heavy load

encapsulation

Initialization and cleanup

python A hundred questions and answers

Spark A hundred questions and answers