Why you should use Deep Learning Online Test?

The Dyb læringstest makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects: Evne til at opbygge og træne neurale netværk Forståelse af datalormaliseringsteknikker Kendskab til forskellige omkostningsfunktioner og aktiveringsfunktioner Færdighed i implementering af backpropagation Kapacitet til at designe og optimere konvolutionale neurale netværk Fortrolighed med tilbagevendende neurale netværk og deres applikationer Forståelse af generative modstridende netværk og deres komponenter Kendskab til naturlige sprogbehandlingsteknikker Færdighed i computervisionsalgoritmer og teknikker Evne til at anvende overførselslæring i dyb læringsmodeller

Deep Learning Online Test

Q: What topics are covered in the Deep Learning Online Test?

Neurale netværk Datalormalisering Omkostningsfunktioner og aktiveringsfunktioner Backpropagation Neurale netværk Konvolutionale neurale netværk Gentagne neurale netværk Generative modstridende netværk Naturlig sprogbehandling Computervision Overføring af læring Autoencoders Optimeringsalgoritmer

About the test:

Den dybe læringstest før ansættelse evaluerer en kandidats forståelse af kerne dybe læringskoncepter som aktiveringsfunktioner, backpropagation, RNNS & CNNS, læringsfrekvens, frafald, batch-normalisering, databehandlingsrørledninger, flerlags perceptrons og datalormalisering. Denne test fokuserer også på deres evne til at anvende dybe læringsalgoritmer til at bruge sager som computervision, billedgenkendelse, objektdetektion, tekstklassificering osv.

Covered skills:

Neurale netværk
Omkostningsfunktioner og aktiveringsfunktioner
Neurale netværk
Gentagne neurale netværk
Naturlig sprogbehandling
Overføring af læring
Optimeringsalgoritmer

Datalormalisering
Backpropagation
Konvolutionale neurale netværk
Generative modstridende netværk
Computervision
Autoencoders

Try practice test

Start 14-day free trial

9 reasons why

Adaface Deep Learning Test is the most accurate way to shortlist Dataforskers

Reason #1

Tests for on-the-job skills

The Deep Learning Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Forståelse og implementering af neurale netværk
Anvendelse af datanormaliseringsteknikker
Valg af passende omkostningsfunktioner og aktiveringsfunktioner
Implementering af backpropagationsalgoritme
Design og evaluering af konvolutionale neurale netværk
Udvikling af tilbagevendende neurale netværk
Oprettelse af generative modstridende netværk
Anvendelse af naturlige sprogbehandlingsteknikker
Implementering af computervisionsalgoritmer
Forståelse og implementering af overførselsindlæring
Udvikling af autoencodere
Optimering af dybe læringsmodeller ved hjælp af optimeringsalgoritmer

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface

Try practice test

Start 14-day free trial

Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

Dette er kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Dyb læringstest vil være ikke-gåbart.

🧐 Question
Medium Changed decision boundary	Try practice test
We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with? The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend? A: Perceptron B: SVM C: Logistic regression D: Guassion discriminant analysis
Medium CNN Architecture Tuning Convolutional Neural Networks Hyperparameter Optimization	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: The model is trained using the following parameters: - Batch size: 64 - Learning rate: 0.001 - Optimizer: Adam - Loss function: Categorical cross-entropy After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance? A: Increase the batch size to 128 B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer C: Replace Adam optimizer with SGD (Stochastic Gradient Descent) D: Decrease the number of filters in each Conv2D layer by half E: Increase the learning rate to 0.01 F: Reduce the size of the Dense layer to 64 units
Medium CNN for Imbalanced Image Dataset Convolutional Neural Networks Imbalanced Datasets	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations: Initial Setup: - CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers. - Activation function: ReLU - No class-weighting or data augmentation. - Results: High overall accuracy, but poor precision and recall for minority classes. Experiment 1: - Changes: Implement class-weighting to penalize mistakes on minority classes more heavily. - Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased. Experiment 2: - Changes: Add dropout layers with a rate of 0.5 after each convolutional layer. - Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes. Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy? A: Increase the dropout rate to 0.7 B: Further fine-tune class-weighting parameters C: Increase the number of filters in the convolutional layers D: Add batch normalization layers after each convolutional layer E: Use a different activation function like LeakyReLU F: Implement more aggressive data augmentation on the minority class
Easy Gradient descent optimization Gradient Descent	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
Medium Less complex decision tree model Model Complexity Overfitting	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
Easy n-gram generator	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
Easy Sensitivity and Specificity Confusion Matrix Model Evaluation	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill
	Medium Changed decision boundary	2 mins Deep Learning	Try practice test
We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with? The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend? A: Perceptron B: SVM C: Logistic regression D: Guassion discriminant analysis
	Medium CNN Architecture Tuning Convolutional Neural Networks Hyperparameter Optimization	3 mins Deep Learning	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: The model is trained using the following parameters: - Batch size: 64 - Learning rate: 0.001 - Optimizer: Adam - Loss function: Categorical cross-entropy After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance? A: Increase the batch size to 128 B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer C: Replace Adam optimizer with SGD (Stochastic Gradient Descent) D: Decrease the number of filters in each Conv2D layer by half E: Increase the learning rate to 0.01 F: Reduce the size of the Dense layer to 64 units
	Medium CNN for Imbalanced Image Dataset Convolutional Neural Networks Imbalanced Datasets	3 mins Deep Learning	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations: Initial Setup: - CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers. - Activation function: ReLU - No class-weighting or data augmentation. - Results: High overall accuracy, but poor precision and recall for minority classes. Experiment 1: - Changes: Implement class-weighting to penalize mistakes on minority classes more heavily. - Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased. Experiment 2: - Changes: Add dropout layers with a rate of 0.5 after each convolutional layer. - Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes. Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy? A: Increase the dropout rate to 0.7 B: Further fine-tune class-weighting parameters C: Increase the number of filters in the convolutional layers D: Add batch normalization layers after each convolutional layer E: Use a different activation function like LeakyReLU F: Implement more aggressive data augmentation on the minority class
	Easy Gradient descent optimization Gradient Descent	2 mins Machine Learning	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Medium Less complex decision tree model Model Complexity Overfitting	2 mins Machine Learning	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	Easy n-gram generator	2 mins Machine Learning	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Easy Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	2 mins Machine Learning	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Easy Sensitivity and Specificity Confusion Matrix Model Evaluation	2 mins Machine Learning	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Changed decision boundary	Deep Learning	Medium	2 mins	Try practice test
We have trained a model on a linearly separable training set to classify the data points into 2 sets (binary classification). Our intern recently labelled some new data points which are all correctly classified by the model. All of the new data points lie far away from the decision boundary. We added these new data points and re-trained our model- our decision boundary changed. Which of these models do you think we could be working with? The 2 data sources use SQL Server and have a 3-character CompanyCode column. Both data sources contain an ORDER BY clause to sort the data by CompanyCode in ascending order. Teylor wants to make sure that the Merge Join transformation works without additional transformations. What would you recommend? A: Perceptron B: SVM C: Logistic regression D: Guassion discriminant analysis
	CNN Architecture Tuning Convolutional Neural Networks Hyperparameter Optimization	Deep Learning	Medium	3 mins	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for image classification. The network architecture is as follows: The model is trained using the following parameters: - Batch size: 64 - Learning rate: 0.001 - Optimizer: Adam - Loss function: Categorical cross-entropy After several training epochs, you observe that the training accuracy is high, but the validation accuracy plateaus and is significantly lower. This suggests possible overfitting. Which of the following adjustments would most effectively mitigate this issue without overly compromising the model's performance? A: Increase the batch size to 128 B: Add dropout layers with a dropout rate of 0.5 after each MaxPooling2D layer C: Replace Adam optimizer with SGD (Stochastic Gradient Descent) D: Decrease the number of filters in each Conv2D layer by half E: Increase the learning rate to 0.01 F: Reduce the size of the Dense layer to 64 units
	CNN for Imbalanced Image Dataset Convolutional Neural Networks Imbalanced Datasets	Deep Learning	Medium	3 mins	Try practice test
You are fine-tuning a Convolutional Neural Network (CNN) for an image classification task where the dataset is highly imbalanced. The majority class comprises 70% of the data. The initial model setup and subsequent experiments yield the following observations: Initial Setup: - CNN architecture: 6 convolutional layers with increasing filter sizes, followed by 2 fully connected layers. - Activation function: ReLU - No class-weighting or data augmentation. - Results: High overall accuracy, but poor precision and recall for minority classes. Experiment 1: - Changes: Implement class-weighting to penalize mistakes on minority classes more heavily. - Results: Improved precision and recall for minority classes, but overall accuracy slightly decreased. Experiment 2: - Changes: Add dropout layers with a rate of 0.5 after each convolutional layer. - Results: Overall accuracy decreased, and no significant change in precision and recall for minority classes. Given these outcomes, what is the most effective strategy to further improve the model's performance specifically for the minority classes without compromising the overall accuracy? A: Increase the dropout rate to 0.7 B: Further fine-tune class-weighting parameters C: Increase the number of filters in the convolutional layers D: Add batch normalization layers after each convolutional layer E: Use a different activation function like LeakyReLU F: Implement more aggressive data augmentation on the minority class
	Gradient descent optimization Gradient Descent	Machine Learning	Easy	2 mins	Try practice test
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network: Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights? A: Constant learning rate of 0.01 B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99 C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99 D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs F: Constant learning rate of 0.1
	Less complex decision tree model Model Complexity Overfitting	Machine Learning	Medium	2 mins	Try practice test
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model: Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting? A: max_depth=5, min_samples_split=2, min_samples_leaf=1 B: max_depth=None, min_samples_split=5, min_samples_leaf=5 C: max_depth=3, min_samples_split=2, min_samples_leaf=1 D: max_depth=None, min_samples_split=2, min_samples_leaf=1 E: max_depth=3, min_samples_split=10, min_samples_leaf=10 F; max_depth=5, min_samples_split=5, min_samples_leaf=5
	n-gram generator	Machine Learning	Easy	2 mins	Try practice test
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
	Recommendation System Selection Recommender Systems Collaborative Filtering Content-Based Filtering	Machine Learning	Easy	2 mins	Try practice test
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?
	Sensitivity and Specificity Confusion Matrix Model Evaluation	Machine Learning	Easy	2 mins	Try practice test
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set. After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results: Based on the confusion matrix, what are the sensitivity and specificity of the model?

Reason #4

1200+ customers in 75 countries

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op mod 75 %, hvilket frigjorde kostbar tid for både ansættelsesledere og vores talentanskaffelsesteam!

Brandon Lee, Leder af mennesker, Love, Bonito

Try practice test

Start 14-day free trial

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Dyb læringstest in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the Dyb læringstest from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

Reason #7

Detailed scorecards & benchmarks

Se prøvescorekort

Try practice test

Start 14-day free trial

Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

Reason #9

Advanced Proctoring

Learn more

Preview this test

Se prøvescorekort

About the Deep Learning Assessment Test

Why you should use Pre-employment Deep Learning Online Test?

The Dyb læringstest makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

Evne til at opbygge og træne neurale netværk
Forståelse af datalormaliseringsteknikker
Kendskab til forskellige omkostningsfunktioner og aktiveringsfunktioner
Færdighed i implementering af backpropagation
Kapacitet til at designe og optimere konvolutionale neurale netværk
Fortrolighed med tilbagevendende neurale netværk og deres applikationer
Forståelse af generative modstridende netværk og deres komponenter
Kendskab til naturlige sprogbehandlingsteknikker
Færdighed i computervisionsalgoritmer og teknikker
Evne til at anvende overførselslæring i dyb læringsmodeller

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Deep Learning Online Test?

Data -normalisering
Datalormalisering er en teknik, der bruges til at standardisere intervallet af dataværdier. Det involverer at omdanne dataene til at have en konsekvent skala, typisk mellem 0 og 1. Denne færdighed måles i denne test for at evaluere muligheden for at forbehandle data effektivt, hvilket er afgørende for at uddanne nøjagtige neurale netværk.
omkostninger Funktioner og aktiveringsfunktioner
Omkostningsfunktioner bruges til at måle forskellen mellem forudsagte og faktiske værdier i et neuralt netværk, der styrer læringsprocessen. Aktiveringsfunktioner introducerer ikke-linearitet til output fra hver neuron i et neuralt netværk, hvilket muliggør komplekse beregninger. Denne færdighed måles i denne test for at vurdere viden om at vælge passende omkostninger og aktiveringsfunktioner til forskellige opgaver.
backpropagation
backpropagation er en nøglealgoritme til træning af neurale netværk. Det beregner gradienterne af netværkets parametre med hensyn til tabet, hvilket muliggør justering af vægte i tidligere lag. Denne færdighed måles i denne test for at måle forståelsen af, hvordan gradienter forplantes bagud gennem et neuralt netværk til effektiv læring.
indviklede neurale netværk </H4> <p> konvolutionale neurale netværk (CNN'er) er dyb læring Modeller specifikt designet til behandling af strukturerede gitterdata, såsom billeder. De er bygget på ideen om konvolution, hvor filtre scanner og udtrækker lokale mønstre fra inputdata. Denne færdighed måles i denne test for at evaluere viden om CNN -arkitektur og dens anvendelse i computervisionsopgaver. </p> <h4> tilbagevendende neurale netværk </H4> <p> Gentagne neurale netværk (RNN'er) er neurale netværk, der processer Sekventielle data med variabel længde, såsom tekst eller tidsserie. De har feedbackforbindelser, der tillader information at fortsætte i hele netværket. Denne færdighed måles i denne test for at vurdere forståelsen af RNN'er og deres evne til at modellere sekventielle mønstre. </p> <h4> Generative modstridende netværk </H4> <p> Generative modstridende netværk (GANS) består af to neurale netværk: A generator og en diskriminator. De trænes sammen i en konkurrencedygtig proces, hvor generatoren sigter mod at producere syntetiske data, der kan skelnes fra reelle data. Denne færdighed måles i denne test for at evaluere viden om GAN -arkitektur og dens anvendelse i generering af realistiske data. </p> <h4> naturlig sprogbehandling
Natural Language Processing (NLP) involverer interaktionen mellem computere og menneskeligt sprog. Det omfatter opgaver såsom talegenkendelse, tekstklassificering og maskinoversættelse. Denne færdighed måles i denne test for at vurdere forståelsen af NLP-teknikker og deres anvendelse i forskellige sprogrelaterede opgaver.
computervision
Computervision er en gren af kunstig intelligens, der handler med fortolkning af visuel information fra billeder eller videoer. Det involverer opgaver som objektdetektion, billedgenkendelse og billedsegmentering. Denne færdighed måles i denne test for at evaluere viden om computervisionsalgoritmer og deres anvendelse til løsning af visuelle opfattelsesproblemer.
Transfer Learning
Transfer Learning refererer til at udnytte forududdannede modeller på En opgave at forbedre ydeevnen på en anden opgave. Ved at bruge viden, der er opnået fra tidligere opgaver, kan overførselsindlæring reducere mængden af træningsdata og tid, der kræves. Denne færdighed måles i denne test for at vurdere forståelsen af overførsel af indlærte funktioner fra et domæne til et andet.
autoencoders
autoencoder er neurale netværk designet til at rekonstruere inputdata fra en komprimeret repræsentation , kaldet det latente rum. De bruges ofte til uovervåget læring og dimensionalitetsreduktion. Denne færdighed måles i denne test for at evaluere viden om autoencoders og deres anvendelse i opgaver som datakomprimering og afvigelse Netværk ved iterativt justering af modellens parametre for at minimere træningstabet. Eksempler inkluderer stokastisk gradientafstamning (SGD), ADAM og RMSPROP. Denne færdighed måles i denne test for at vurdere fortroligheden med forskellige optimeringsalgoritmer og deres indflydelse på netværkskonvergens og ydeevne.
Full list of covered topics
The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Dyb læringstest to be based on.
Neuron
Gradient afstamning
Feedforward Neural Network
Partiskhed
Aktiveringsfunktion
Vægtinitialisering
Overfitting
Regularisering
Tabsfunktion
Læringshastighed
Batch -normalisering
Droppe ud
Indviklet lag
Pooling
Gentagne neurale netværk
Lstm
Gan
Sprogmodellering
Ord indlejringer
CNN Architecture
Billedklassificering
Objektdetektion
Billedsegmentering
RNN Arkitektur
Tale genkendelse
Sentimentanalyse
Forstærkningslæring
Tekstgenerering
Optimeringsalgoritmer
Adam Optimizer
Stokastisk gradientafstamning
Læringshastighed forfald
Overfør læringsteknikker
Pretrained modeller
Autoencoder Architecture
Dimensionalitetsreduktion
Encoder-decoder
Hyperparameterindstilling
Dataforstørrelse
Regulariserede autoencodere
Støjinjektion
Forsvindende gradientproblem
Generative modeller
GAN -træning
Billedgenerering
Modstridende angreb
CNN -tolkbarhed
Opmærksomhedsmekanismer
Naturlig sprogforståelse
Visuelt spørgsmål besvarelse
Billedtekst
Transformatorer
Bert
Dyb forstærkning læring
Politikgradient
Værdi iteration
Q-learning
Autoencoders til afvigelse
Kunstige neurale netværk

Try practice test

Start 14-day free trial

What roles can I use the Deep Learning Online Test for?

Dataforsker
Machine Learning Engineer
Kunstig intelligensforsker
Deep Learning Engineer
Dataanalytiker
Computer Vision Engineer
Naturlig sprogbehandlingsingeniør
AI -konsulent
Kunstig intelligensroller
Forsker

How is the Deep Learning Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Kendskab til autoencodere og deres applikationer
Færdigheder i optimeringsalgoritmer til neurale netværk
Kapacitet til at implementere gradientafstamning og dens varianter
Forståelse af stokastisk gradientafstamning og dets varianter
Kendskab til læringshastighedsplanlægningsteknikker
Færdighed i batchnormalisering i neurale netværk
Evne til at implementere frafalds regularisering i modeller
Forståelse af vægtinitialiseringsstrategier
Kendskab til tidlig stop i træning af neurale netværk
Færdighed i modelevaluering og valideringsteknikker

Preview this test

Se prøvescorekort

Ansættelseslederne mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, var i stand til at fortælle, hvilke kandidater der havde bedre score og differentieret med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af de kandidater, der er nomineret med Adaface-screeningen.

85%

Reduktion i screeningstid

Se den fulde casestudie

Deep Learning Hiring Test Ofte stillede spørgsmål

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

Ikke-gåbare spørgsmål
IP Proctoring
Webproctoring
Webcam Proctoring
Detektion af plagiering
Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.