Why you should use Deep Learning Online Test?

The Dyp 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 å bygge og trene nevrale nettverk Forståelse av datarormaliseringsteknikker Kunnskap om forskjellige kostnadsfunksjoner og aktiveringsfunksjoner Kompetanse i implementering av backpropagation Evne til å designe og optimalisere konvolusjonelle nevrale nettverk Kjennskap til tilbakevendende nevrale nettverk og deres applikasjoner Forståelse av generative motstridende nettverk og deres komponenter Kunnskap om naturlig språkbehandlingsteknikker Kompetanse i datamaskinvisjonsalgoritmer og teknikker Evne til å anvende overføringslæring i dype læringsmodeller

Deep Learning Online Test

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

Nevrale nettverk Data normalisering Kostnadsfunksjoner og aktiveringsfunksjoner Backpropagation Nevrale nettverk Konvolusjonelle nevrale nettverk Gjentagende nevrale nettverk Generative motstridende nettverk Naturlig språkbehandling Datamaskin syn Overfør læring Autocoders Optimaliseringsalgoritmer

About the test:

Den dype læringsforsyningstesten evaluerer en kandidats forståelse av kjerne dype læringsbegreper som aktiveringsfunksjoner, backpropagation, RNNS & CNN, læringshastighet, frafall, batchnormalisering, databehandlingsrørledninger, flerlags perceptroner og dataporalisering. Denne testen fokuserer også på deres evne til å anvende dype læringsalgoritmer for å bruke tilfeller som datamaskinvisjon, bildegjenkjenning, objektdeteksjon, tekstklassifisering osv.

Covered skills:

Nevrale nettverk
Kostnadsfunksjoner og aktiveringsfunksjoner
Nevrale nettverk
Gjentagende nevrale nettverk
Naturlig språkbehandling
Overfør læring
Optimaliseringsalgoritmer

Data normalisering
Backpropagation
Konvolusjonelle nevrale nettverk
Generative motstridende nettverk
Datamaskin syn
Autocoders

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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:

Forstå og implementere nevrale nettverk
Bruke datarormaliseringsteknikker
Velge passende kostnadsfunksjoner og aktiveringsfunksjoner
Implementering av backpropagation -algoritme
Designe og evaluere konvolusjonelle nevrale nettverk
Utvikle tilbakevendende nevrale nettverk
Opprette generative motstridende nettverk
Bruke naturlige språkbehandlingsteknikker
Implementering av datamaskinvisjonsalgoritmer
Forstå og implementere overføringslæring
Utvikle autoencoders
Optimalisering av dype læringsmodeller ved bruk av optimaliseringsalgoritmer

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

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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 bare en liten prøve fra biblioteket vårt med 10.000+ spørsmål. De faktiske spørsmålene om dette Dyp læringstest vil være ikke-googlable.

🧐 Question
Medium Changed decision boundary	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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	Solve
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 å optimalisere den første screeningsprosessen vår med oppover 75 %, og frigjorde dyrebar tid for både ansettelsesledere og vårt talentanskaffelsesteam!

Brandon Lee, Leder for mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Dyp 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 Dyp 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

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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

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About the Deep Learning Assessment Test

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

The Dyp 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 å bygge og trene nevrale nettverk
Forståelse av datarormaliseringsteknikker
Kunnskap om forskjellige kostnadsfunksjoner og aktiveringsfunksjoner
Kompetanse i implementering av backpropagation
Evne til å designe og optimalisere konvolusjonelle nevrale nettverk
Kjennskap til tilbakevendende nevrale nettverk og deres applikasjoner
Forståelse av generative motstridende nettverk og deres komponenter
Kunnskap om naturlig språkbehandlingsteknikker
Kompetanse i datamaskinvisjonsalgoritmer og teknikker
Evne til å anvende overføringslæring i dype 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?

Neural nettverk
Neural nettverk er en beregningsmodell inspirert av biologiske nevrale nettverk. De er sammensatt av lag med sammenkoblede noder, eller kunstige nevroner, som prosesser og overfører informasjon ved bruk av vektede innganger. De måles i denne testen for å vurdere forståelsen av grunnleggende begreper i dyp læring.
Data -normalisering
Data -normalisering er en teknikk som brukes til å standardisere rekkevidden av dataverdier. Det innebærer å transformere dataene til å ha en jevn skala, typisk mellom 0 og 1. Denne ferdigheten måles i denne testen for å evaluere evnen til å forprosesser data effektivt, noe som er avgjørende for å trene nøyaktige nevrale nettverk.
Kostnad Funksjoner og aktiveringsfunksjoner
Kostnadsfunksjoner brukes til å måle forskjellen mellom forutsagte og faktiske verdier i et nevralt nettverk, og styrer læringsprosessen. Aktiveringsfunksjoner introduserer ikke-linearitet til utgangen fra hvert nevron i et nevralt nettverk, noe som muliggjør komplekse beregninger. Denne ferdigheten måles i denne testen for å vurdere kunnskapen om å velge passende kostnads- og aktiveringsfunksjoner for forskjellige oppgaver.
BackPropagation
BackPropagation er en nøkkelalgoritme for å trene nevrale nettverk. Det beregner gradientene til nettverkets parametere med hensyn til tapet, noe som gir mulighet for justering av vekter i tidligere lag. Denne ferdigheten måles i denne testen for å måle forståelsen av hvordan gradienter forplanter seg bakover gjennom et nevralt nettverk for effektiv læring.
Convolutional Neural Networks
Convolutional Neural Networks (CNN) er dyp læring Modeller spesielt designet for behandling av strukturerte nettdata, for eksempel bilder. De er bygget på ideen om konvolusjon, der filtre skanner og henter ut lokale mønstre fra inngangsdata. Denne ferdigheten måles i denne testen for å evaluere kunnskapen om CNN -arkitektur og dens anvendelse i datamaskinvisjonsoppgaver.
tilbakevendende nevrale nettverk
Gjentakende nevrale nettverk (RNN) er nevrale nettverk som prosessen Sekvensielle data med variabel lengde, for eksempel tekst eller tidsserier. De har tilbakemeldingstilkoblinger som lar informasjon vedvare i hele nettverket. Denne ferdigheten måles i denne testen for å vurdere forståelse av RNN -er og deres evne til å modellere sekvensielle mønstre.
Generative motstridende nettverk
Generative motstridende nettverk (GAN) består av to nevrale nettverk: a generator og en diskriminator. De er trent sammen i en konkurranseprosess, der generatoren tar sikte på å produsere syntetiske data som ikke kan skilles fra reelle data. Denne ferdigheten måles i denne testen for å evaluere kunnskap om GaN -arkitektur og dens anvendelse for å generere realistiske data.
Naturlig språkbehandling
Natural Language Processing (NLP) involverer samspillet mellom datamaskiner og menneskelig språk. Det omfatter oppgaver som talegjenkjenning, tekstklassifisering og maskinoversettelse. Denne ferdigheten måles i denne testen for å vurdere forståelsen av NLP-teknikker og deres anvendelse i forskjellige språkrelaterte oppgaver.
Computer Vision
Computer Vision er en gren av kunstig intelligens som handler med tolking av visuell informasjon fra bilder eller videoer. Det innebærer oppgaver som objektdeteksjon, bildegjenkjenning og bildesegmentering. Denne ferdigheten måles i denne testen for å evaluere kunnskapen om datamaskinvisjonsalgoritmer og deres anvendelse for å løse visuelle persepsjonsproblemer.
Overføringslæring
Overføringslæring refererer til å utnytte forhåndsutdannede modeller på En oppgave å forbedre ytelsen på en annen oppgave. Ved å bruke kunnskap som er oppnådd fra tidligere oppgaver, kan overføringslæring redusere mengden treningsdata og tid som kreves betydelig. Denne ferdigheten måles i denne testen for å vurdere forståelsen av overføring av lærde funksjoner fra ett domene til et annet.
Autocoders
Autocoders er nevrale nettverk designet for å rekonstruere inngangsdataene fra en komprimert representasjon , kalt det latente rommet. De brukes ofte til uovervåket læring og reduksjon av dimensjonalitet. Denne ferdigheten måles i denne testen for å evaluere kunnskapen om autoencoders og deres anvendelse i oppgaver som datakomprimering og anomalideteksjon.
Optimaliseringsalgoritmer
Optimaliseringsalgoritmer spiller en avgjørende rolle i trening av nevrale trening Nettverk ved iterativt å justere modellens parametere for å minimere treningstapet. Eksempler inkluderer stokastisk gradient nedstigning (SGD), Adam og RMSPROP. Denne ferdigheten måles i denne testen for å vurdere kjennskap til forskjellige optimaliseringsalgoritmer og deres innvirkning på nettverkskonvergens og ytelse.
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 Dyp læringstest to be based on.
Nevron
Gradient nedstigning
Feedforward Neural Network
Partiskhet
Aktiveringsfunksjon
Vektinitialisering
Overmontering
Regularisering
Tapsfunksjon
Læringsrate
Batch normalisering
Frafall
Convolutional lag
Bading
Gjentagende nevrale nettverk
LSTM
Gan
Språkmodellering
Ordinnstilling
CNN -arkitektur
Bildeklassifisering
Objektdeteksjon
Bildesegmentering
RNN -arkitektur
Talegjenkjenning
Sentimentanalyse
Forsterkningslæring
Tekstgenerering
Optimaliseringsalgoritmer
Adam Optimizer
Stokastisk gradient nedstigning
Læringsfrekvens forfall
Overfør læringsteknikker
Pretrained modeller
Autoencoder -arkitektur
Dimensjonalitetsreduksjon
Encoder-Decoder
Hyperparameterinnstilling
Dataforstørrelse
Regulariserte autoencoders
Støyinjeksjon
Forsvinnende gradientproblem
Generative modeller
GaN -trening
Bildegenerering
Motstridende angrep
CNN -tolkbarhet
Oppmerksomhetsmekanismer
Naturlig språkforståelse
Visuelt spørsmål svar
Bildebilde
Transformatorer
Bert
Dyp forsterkningslæring
Politikkgradient
Verdi iterasjon
Q-læring
Autocoders for anomalideteksjon
Kunstige nevrale nettverk

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

Dataforsker
Machine Learning Engineer
Kunstig intelligensforsker
Deep Learning Engineer
Data analytiker
Computer Vision Engineer
Naturlig språkbehandlingsingeniør
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

Kunnskap om autoencoders og deres applikasjoner
Kompetanse i optimaliseringsalgoritmer for nevrale nettverk
Evne til å implementere gradientavstamning og dens varianter
Forståelse av stokastisk gradient nedstigning og dens varianter
Kunnskap om læringsfrekvensplanleggingsteknikker
Kompetanse i batchnormalisering i nevrale nettverk
Evne til å implementere frafallsregularisering i modeller
Forståelse av vektinitialiseringsstrategier
Kunnskap om tidlig stopp i trening av nevrale nettverk
Kompetanse i modellevaluering og valideringsteknikker

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Ansettelseslederne mente at de gjennom de tekniske spørsmålene de stilte under panelintervjuene, var i stand til å fortelle hvilke kandidater som scoret bedre, og differensierte med de som ikke skåret like godt. De er svært fornøyd med kvaliteten på kandidatene som er på listen med Adaface-screeningen.

85%

Reduksjon i screeningstid

Se hele casestudien

Deep Learning Hiring Test Vanlige spørsmål

Kan jeg kombinere flere ferdigheter til en tilpasset vurdering?

Ja absolutt. Tilpassede vurderinger er satt opp basert på stillingsbeskrivelsen din, og vil inneholde spørsmål om alle må-ha ferdigheter du spesifiserer.

Har du noen anti-juksende eller proktoreringsfunksjoner på plass?

Vi har følgende anti-juksede funksjoner på plass:

Ikke-googlable spørsmål
IP Proctoring
Nettproctoring
Webcam Proctoring
Deteksjon av plagiering
Sikker nettleser

Les mer om Proctoring -funksjonene.

Hvordan tolker jeg testresultater?

Den viktigste tingen å huske på er at en vurdering er et eliminasjonsverktøy, ikke et seleksjonsverktøy. En ferdighetsvurdering er optimalisert for å hjelpe deg med å eliminere kandidater som ikke er teknisk kvalifisert for rollen, det er ikke optimalisert for å hjelpe deg med å finne den beste kandidaten for rollen. Så den ideelle måten å bruke en vurdering på er å bestemme en terskelpoeng (vanligvis 55%, vi hjelper deg med å benchmark) og invitere alle kandidater som scorer over terskelen for de neste rundene med intervjuet.

Hvilken opplevelsesnivå kan jeg bruke denne testen til?

Hver ADAFACE -vurdering er tilpasset din stillingsbeskrivelse/ ideell kandidatperson (våre fageksperter vil velge de riktige spørsmålene for din vurdering fra vårt bibliotek med 10000+ spørsmål). Denne vurderingen kan tilpasses for ethvert opplevelsesnivå.

Får hver kandidat de samme spørsmålene?

Ja, det gjør det mye lettere for deg å sammenligne kandidater. Alternativer for MCQ -spørsmål og rekkefølgen på spørsmål er randomisert. Vi har anti-juksing/proctoring funksjoner på plass. I vår bedriftsplan har vi også muligheten til å lage flere versjoner av den samme vurderingen med spørsmål med lignende vanskelighetsnivåer.