Why you should use Deep Learning Online Test?

The Djup inlärningstest 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: Förmåga att bygga och träna neurala nätverk Förståelse av datanormaliseringstekniker Kunskap om olika kostnadsfunktioner och aktiveringsfunktioner Kunskaper i implementering av backpropagation Förmåga att designa och optimera konvolutionella neurala nätverk Bekanta med återkommande neurala nätverk och deras applikationer Förståelse av generativa motsatsnätverk och deras komponenter Kunskap om naturliga språkbearbetningstekniker Kunskaper i datorsynsalgoritmer och tekniker Möjlighet att tillämpa överföringsinlärning i djupa inlärningsmodeller

Deep Learning Online Test

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

Neurala nätverk Datanormalisering Kostnadsfunktioner och aktiveringsfunktioner Backpropagation Neurala nätverk Konvolutionella neurala nätverk Återkommande neurala nätverk Generativa motsatsnätverk Naturlig språkbehandling Datorsyn Överföringslärande Autokodare Optimeringsalgoritmer

About the test:

Det djupa inlärningstestet för anställning utvärderar en kandidats förståelse av kärndjupinlärningskoncept som aktiveringsfunktioner, backpropagation, RNNS & CNNS, inlärningshastighet, bortfall, batchnormalisering, databehandlingsrörledningar, multilagers perceptron och datanormalisering. Detta test fokuserar också på deras förmåga att tillämpa djupa inlärningsalgoritmer för att använda fall som datorsyn, bildigenkänning, objektdetektering, textklassificering etc.

Covered skills:

Neurala nätverk
Kostnadsfunktioner och aktiveringsfunktioner
Neurala nätverk
Återkommande neurala nätverk
Naturlig språkbehandling
Överföringslärande
Optimeringsalgoritmer

Datanormalisering
Backpropagation
Konvolutionella neurala nätverk
Generativa motsatsnätverk
Datorsyn
Autokodare

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9 reasons why

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

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:

Förstå och implementera neurala nätverk
Tillämpa datanormaliseringstekniker
Välja lämpliga kostnadsfunktioner och aktiveringsfunktioner
Implementera backpropagationsalgoritm
Designa och utvärdera konvolutionella neurala nätverk
Utveckla återkommande neurala nätverk
Skapa generativa motståndare
Tillämpa naturliga språkbearbetningstekniker
Implementera datorsynsalgoritmer
Förståelse och implementering av överföringslärande
Utveckla autoenkoder
Optimera djupa inlärningsmodeller med 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

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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

Dessa är bara ett litet urval från vårt bibliotek med 10 000+ frågor. De faktiska frågorna om detta Djup inlärningstest kommer att vara icke-googleable.

🧐 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 kunde vi optimera vår initiala screeningprocess med uppemot 75 %, vilket frigjorde dyrbar tid för både anställande chefer och vårt team för att förvärva talang!

Brandon Lee, Chef för människor, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Djup inlärningstest 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 Djup inlärningstest 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 Djup inlärningstest 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:

Förmåga att bygga och träna neurala nätverk
Förståelse av datanormaliseringstekniker
Kunskap om olika kostnadsfunktioner och aktiveringsfunktioner
Kunskaper i implementering av backpropagation
Förmåga att designa och optimera konvolutionella neurala nätverk
Bekanta med återkommande neurala nätverk och deras applikationer
Förståelse av generativa motsatsnätverk och deras komponenter
Kunskap om naturliga språkbearbetningstekniker
Kunskaper i datorsynsalgoritmer och tekniker
Möjlighet att tillämpa överföringsinlärning i djupa inlärningsmodeller

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 Normalization
Normalisering är en teknik som används för att standardisera datavärden. Det handlar om att omvandla data för att ha en konsekvent skala, vanligtvis mellan 0 och 1. Denna färdighet mäts i detta test för att utvärdera förmågan att förbehandla data effektivt, vilket är avgörande för att träna exakta neurala nätverk.
Kostnad Funktioner och aktiveringsfunktioner
Kostnadsfunktioner används för att mäta skillnaden mellan förutsagda och faktiska värden i ett neuralt nätverk, vilket styr inlärningsprocessen. Aktiveringsfunktioner introducerar icke-linearitet för utgången från varje neuron i ett neuralt nätverk, vilket möjliggör komplexa beräkningar. Denna färdighet mäts i detta test för att bedöma kunskapen om att välja lämpliga kostnads- och aktiveringsfunktioner för olika uppgifter.
Backpropagation
Backpropagation är en nyckelalgoritm för träningsnätverk. Den beräknar lutningarna i nätverkets parametrar med avseende på förlusten, vilket möjliggör justering av vikter i tidigare lager. Denna färdighet mäts i detta test för att mäta förståelsen för hur lutningar sprider sig bakåt genom ett neuralt nätverk för effektivt lärande.
Convolutional Neural Networks
Convolutional Neural Networks (CNNS) är djupt lärande Modeller specifikt utformade för att bearbeta strukturerade rutdata, till exempel bilder. De är byggda på idén om upplösning, där filter skannar och extraherar lokala mönster från inmatningsdata. Denna färdighet mäts i detta test för att utvärdera kunskapen om CNN -arkitektur och dess tillämpning i datorsynsuppgifter.
Återkommande neurala nätverk
Återkommande neurala nätverk (RNN) är neurala nätverk som bearbetar bearbetar Sekventiella data med variabel längd, såsom text eller tidsserie. De har feedbackanslutningar som gör att information kan fortsätta i hela nätverket. Denna färdighet mäts i detta test för att bedöma förståelsen av RNN: er och deras förmåga att modellera sekventiella mönster.
Generativa motsatta nätverk
Generativa motståndsnätverk (GAN) består av två neurala nätverk: A generator och en diskriminator. De tränas tillsammans i en konkurrensprocess, där generatorn syftar till att producera syntetiska data som inte kan skiljas från verkliga data. Denna färdighet mäts i detta test för att utvärdera kunskap om GAN -arkitektur och dess tillämpning vid generering av realistiska data.
Natural Language Processing
Natural Language Processing (NLP) innebär interaktion mellan datorer och mänskligt språk. Det omfattar uppgifter som taligenkänning, textklassificering och maskinöversättning. Denna färdighet mäts i detta test för att bedöma förståelsen för NLP-tekniker och deras tillämpning i olika språkrelaterade uppgifter.
Datorvision
Datorvision är en gren av konstgjord intelligens som handlar med att tolka visuell information från bilder eller videor. Det handlar om uppgifter som objektdetektering, bildigenkänning och bildsegmentering. Denna färdighet mäts i detta test för att utvärdera kunskapen om datorsynsalgoritmer och deras tillämpning vid lösning av visuella uppfattningar. En uppgift att förbättra prestandan på en annan uppgift. Genom att använda kunskap som erhållits från tidigare uppgifter kan överföringsinlärning avsevärt minska mängden utbildningsdata och tid som krävs. Denna färdighet mäts i detta test för att bedöma förståelsen för att överföra lärda funktioner från en domän till en annan.
autoencoders
autoencoders är neurala nätverk utformade för att rekonstruera inmatningsdata från en komprimerad representation , kallad det latenta utrymmet. De används ofta för minskning av lärande och dimensionalitet. Denna färdighet mäts i detta test för att utvärdera kunskapen om autokodare och deras tillämpning i uppgifter som datakomprimering och anomalidetektering.
Optimeringsalgoritmer
Optimeringsalgoritmer spelar en avgörande roll i träningsneural neural nätverk genom att iterativt justera modellens parametrar för att minimera träningsförlusten. Exempel inkluderar stokastisk gradient härkomst (SGD), Adam och RMSProp. Denna färdighet mäts i detta test för att bedöma kännedom om olika optimeringsalgoritmer och deras påverkan på nätverkskonvergens och prestanda.
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 Djup inlärningstest to be based on.
Nervcell
Lutning
Fashion Forward Neural Network
Partiskhet
Aktiveringsfunktion
Viktinitiering
Överanpassning
Regulerisering
Förlustfunktion
Inlärningshastighet
Normalisering
Hoppa av
Upplösningsskikt
Folding
Återkommande neuralt nätverk
Lstm
Gan
Språkmodellering
Ordinbäddningar
CNN -arkitektur
Bildklassificering
Objektdetektering
Bildsegmentering
RNN -arkitektur
Taligenkänning
Känsla analys
Förstärkningsinlärning
Textproduktion
Optimeringsalgoritmer
Adam Optimizer
Stokastisk lutning
Förfall av inlärningshastighet
Överföra inlärningstekniker
Fördröjda modeller
Autoencoderarkitektur
Dimensionalitetsminskning
Kodarkoder
Hyperparameterinställning
Dataförstoring
Regelbundna autoenkoder
Bullerinjektion
Försvinnande gradientproblem
Generativa modeller
Gan -utbildning
Bildgenerering
Motsatsattacker
CNN -tolkbarhet
Uppmärksamhetsmekanismer
Naturlig språkförståelse
Visuell fråga svar
Bildtexter
Transformatorer
Bert
Djup förstärkningslärande
Policygradient
Värdering
Q-learning
Autoenkoder för avvikelse av anomali
Artificiellt nervsystem

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

Datavetare
Maskininlärningsingenjör
Forskare för konstgjord intelligens
Djup inlärningsingenjör
Dataanalytiker
Datorvisionstekniker
Naturligt språkbearbetningsingenjör
AI Consultant
Konstgjorda roller
Forskare

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

Kunskap om autoenkodare och deras tillämpningar
Kunskaper i optimeringsalgoritmer för neurala nätverk
Förmåga att implementera gradient härkomst och dess varianter
Förståelse av stokastisk gradient härkomst och dess varianter
Kunskap om schemaläggningstekniker för inlärningshastighet
Kunskaper i batchnormalisering i neurala nätverk
Möjlighet att implementera bortfallsreglering i modeller
Förståelse för initialiseringsstrategier
Kunskap om tidigt stopp i träningsnätverk
Kunskaper i modellutvärdering och valideringstekniker

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De rekryterande cheferna upplevde att de genom de tekniska frågorna som de ställde under panelintervjuerna kunde berätta vilka kandidater som hade bättre poäng och särskiljde sig med de som inte fick lika bra poäng. Dom är mycket nöjd med kvaliteten på de kandidater som nominerades med Adaface-screeningen.

85%

minskning av screeningstiden

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Deep Learning Hiring Test Vanliga frågor

Kan jag kombinera flera färdigheter till en anpassad bedömning?

Ja absolut. Anpassade bedömningar ställs in baserat på din arbetsbeskrivning och kommer att innehålla frågor om alla måste-ha färdigheter du anger.

Har du några anti-cheating eller proctoring-funktioner på plats?

Vi har följande anti-cheating-funktioner på plats:

Icke-Googleable-frågor
IP -proctoring
webbproctoring
webbkamera proctoring
Detektion av plagiering
säker webbläsare

Läs mer om proctoring -funktionerna.

Hur tolkar jag testresultat?

Det främsta att tänka på är att en bedömning är ett eliminationsverktyg, inte ett urvalsverktyg. En kompetensbedömning är optimerad för att hjälpa dig att eliminera kandidater som inte är tekniskt kvalificerade för rollen, den är inte optimerad för att hjälpa dig hitta den bästa kandidaten för rollen. Så det ideala sättet att använda en bedömning är att bestämma en tröskelpoäng (vanligtvis 55%, vi hjälper dig att jämföra) och bjuda in alla kandidater som gör poäng över tröskeln för nästa intervjurundor.

Vilken erfarenhetsnivå kan jag använda detta test för?

Varje AdaFace -bedömning anpassas till din arbetsbeskrivning/ idealisk kandidatperson (våra ämnesexperter kommer att välja rätt frågor för din bedömning från vårt bibliotek med 10000+ frågor). Denna bedömning kan anpassas för alla erfarenhetsnivåer.

Får varje kandidat samma frågor?

Ja, det gör det mycket lättare för dig att jämföra kandidater. Alternativ för MCQ -frågor och ordningen på frågor randomiseras. Vi har anti-cheating/proctoring -funktioner på plats. I vår företagsplan har vi också möjlighet att skapa flera versioner av samma bedömning med frågor om liknande svårighetsnivåer.

Jag är kandidat. Kan jag prova ett träningstest?

Nej. Tyvärr stöder vi inte övningstester just nu. Du kan dock använda våra exempelfrågor för övning.

Vad är kostnaden för att använda detta test?

Du kan kolla in våra prisplaner.

Kan jag få en gratis provperiod?

Plattformen är helt självbetjänande, så här är ett sätt att gå vidare:

Du kan registrera dig gratis för att få en känsla för hur det fungerar.
Den kostnadsfria provperioden inkluderar en provbedömning (Java/JavaScript) som du hittar i din instrumentpanel när du registrerar dig. Du kan använda den för att granska kvaliteten på frågorna och kandidaternas upplevelse av ett konversationstest på Adaface.
För att granska kvaliteten på frågorna kan du också granska våra offentliga frågor för 50+ färdigheter här.
När du är övertygad om att du vill testa det med riktiga bedömningar och kandidater kan du välja en plan enligt dina krav.