Why you should use Deep Learning Online Test?

The Prueba de aprendizaje profundo 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: Capacidad para construir y entrenar redes neuronales Comprensión de las técnicas de normalización de datos Conocimiento de diversas funciones de costos y funciones de activación Competencia en la implementación de backpropagation Capacidad para diseñar y optimizar redes neuronales convolucionales Familiaridad con las redes neuronales recurrentes y sus aplicaciones Comprensión de las redes adversas generativas y sus componentes Conocimiento de las técnicas de procesamiento del lenguaje natural Competencia en algoritmos y técnicas de visión por computadora Capacidad para aplicar el aprendizaje de transferencia en modelos de aprendizaje profundo

Deep Learning Online Test

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

Redes neuronales Normalización de datos Funciones de costos y funciones de activación Backpropagation Redes neuronales Redes neuronales convolucionales Redes neuronales recurrentes Redes adversas generativas Procesamiento natural del lenguaje Visión por computador Transferir el aprendizaje Autoencoders Algoritmos de optimización

About the test:

La prueba de pre-empleo de aprendizaje profundo evalúa la comprensión de un candidato de conceptos centrales de aprendizaje profundo como funciones de activación, backpropagation, RNNS y CNNS, tasa de aprendizaje, deserción, normalización por lotes, tuberías de procesamiento de datos, perceptrones de múltiples capas y normalización de datos. Esta prueba también se centra en su capacidad para aplicar algoritmos de aprendizaje profundo para usar casos como visión por computadora, reconocimiento de imágenes, detección de objetos, clasificación de texto, etc.

Covered skills:

Redes neuronales
Funciones de costos y funciones de activación
Redes neuronales
Redes neuronales recurrentes
Procesamiento natural del lenguaje
Transferir el aprendizaje
Algoritmos de optimización

Normalización de datos
Backpropagation
Redes neuronales convolucionales
Redes adversas generativas
Visión por computador
Autoencoders

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9 reasons why

Adaface Deep Learning Test is the most accurate way to shortlist Científico de datoss

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:

Comprensión e implementación de redes neuronales
Aplicación de técnicas de normalización de datos
Seleccionar funciones de costo apropiadas y funciones de activación
Implementación del algoritmo de backpropagation
Diseño y evaluación de redes neuronales convolucionales
Desarrollo de redes neuronales recurrentes
Creación de redes adversas generativas
Aplicación de técnicas de procesamiento del lenguaje natural
Implementación de algoritmos de visión por computadora
Comprensión e implementación del aprendizaje de transferencia
Desarrollo de autoencoders
Optimización de modelos de aprendizaje profundo utilizando algoritmos de optimización

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

Estas son solo una pequeña muestra de nuestra biblioteca de más de 10,000 preguntas. Las preguntas reales sobre esto Prueba de aprendizaje profundo no se puede obtener.

🧐 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

Con Adaface, pudimos optimizar nuestro proceso de selección inicial en más de un 75 %, liberando un tiempo precioso tanto para los gerentes de contratación como para nuestro equipo de adquisición de talentos.

Brandon Lee, jefe de personas, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Prueba de aprendizaje profundo 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 Prueba de aprendizaje profundo 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 Prueba de aprendizaje profundo 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:

Capacidad para construir y entrenar redes neuronales
Comprensión de las técnicas de normalización de datos
Conocimiento de diversas funciones de costos y funciones de activación
Competencia en la implementación de backpropagation
Capacidad para diseñar y optimizar redes neuronales convolucionales
Familiaridad con las redes neuronales recurrentes y sus aplicaciones
Comprensión de las redes adversas generativas y sus componentes
Conocimiento de las técnicas de procesamiento del lenguaje natural
Competencia en algoritmos y técnicas de visión por computadora
Capacidad para aplicar el aprendizaje de transferencia en modelos de aprendizaje profundo

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?

redes neuronales
Las redes neuronales son un modelo computacional inspirado en redes neuronales biológicas. Están compuestos por capas de nodos interconectados, o neuronas artificiales, que procesan y transmiten información utilizando entradas ponderadas. Se miden en esta prueba para evaluar la comprensión de los conceptos fundamentales en el aprendizaje profundo.
Normalización de datos
La normalización de datos es una técnica utilizada para estandarizar el rango de valores de datos. Implica transformar los datos para tener una escala consistente, típicamente entre 0 y 1. Esta habilidad se mide en esta prueba para evaluar la capacidad de preprocesar los datos de manera efectiva, lo cual es crucial para capacitar a redes neuronales precisas.
costo de costo Funciones y funciones de activación
Las funciones de costo se utilizan para medir la diferencia entre los valores predichos y reales en una red neuronal, guiando el proceso de aprendizaje. Las funciones de activación introducen no linealidad en la salida de cada neurona en una red neuronal, lo que permite cálculos complejos. Esta habilidad se mide en esta prueba para evaluar el conocimiento de seleccionar las funciones apropiadas de costos y activación para diferentes tareas.
backpropagation
Backpropagation es un algoritmo clave para capacitar a las redes neuronales. Calcula los gradientes de los parámetros de la red con respecto a la pérdida, lo que permite el ajuste de pesos en capas anteriores. Esta habilidad se mide en esta prueba para medir la comprensión de cómo los gradientes se propagan hacia atrás a través de una red neuronal para un aprendizaje eficiente.
redes neuronales convolucionales
redes neuronales convolucionales (CNN) son aprendizaje profundo Modelos diseñados específicamente para procesar datos de cuadrícula estructurados, como imágenes. Se basan en la idea de la convolución, donde los filtros escanean y extraen patrones locales de los datos de entrada. Esta habilidad se mide en esta prueba para evaluar el conocimiento de la arquitectura CNN y su aplicación en las tareas de visión por computadora.
redes neuronales recurrentes
redes neuronales recurrentes (RNN) son redes neuronales que procesan Datos secuenciales de longitud variable, como texto o serie de tiempo. Tienen conexiones de retroalimentación que permiten que la información persista en toda la red. Esta habilidad se mide en esta prueba para evaluar la comprensión de los RNN y su capacidad para modelar patrones secuenciales.
Redes adversas generativas
Las redes adversas generativas (GANS) consisten en dos redes neuronales: A generador y discriminador. Están capacitados juntos en un proceso competitivo, donde el generador tiene como objetivo producir datos sintéticos que no sean indistinguibles de los datos reales. Esta habilidad se mide en esta prueba para evaluar el conocimiento de la arquitectura de GaN y su aplicación en la generación de datos realistas.
Procesamiento del lenguaje natural
El procesamiento del lenguaje natural (NLP) implica la interacción entre las computadoras y lenguaje humano. Aligue tareas como el reconocimiento de voz, la clasificación de texto y la traducción automática. Esta habilidad se mide en esta prueba para evaluar la comprensión de las técnicas de PNL y su aplicación en varias tareas relacionadas con el lenguaje.
Visión de la computadora
La visión por computadora es una rama de la inteligencia artificial que trata Con la interpretación de información visual de imágenes o videos. Implica tareas como detección de objetos, reconocimiento de imágenes y segmentación de imágenes. Esta habilidad se mide en esta prueba para evaluar el conocimiento de los algoritmos de visión por computadora y su aplicación en la resolución de problemas de percepción visual.
Aprendizaje de transferencia
El aprendizaje de transferencia se refiere a aprovechar modelos previamente capacitados en Una tarea para mejorar el rendimiento en otra tarea. Al utilizar el conocimiento obtenido de tareas anteriores, el aprendizaje de transferencia puede reducir significativamente la cantidad de datos de capacitación y el tiempo requeridos. Esta habilidad se mide en esta prueba para evaluar la comprensión de la transferencia de características aprendidas de un dominio a otro.
Autoencoders
Los autoinscodadores son redes neuronales diseñadas para reconstruir los datos de entrada de una representación comprimida , llamado el espacio latente. A menudo se usan para el aprendizaje no supervisado y la reducción de la dimensionalidad. Esta habilidad se mide en esta prueba para evaluar el conocimiento de los autoencoders y su aplicación en tareas como la compresión de datos y la detección de anomalías.
Los algoritmos de optimización
Los algoritmos de optimización juegan un papel crucial en el entrenamiento neural neural Redes ajustando iterativamente los parámetros del modelo para minimizar la pérdida de entrenamiento. Los ejemplos incluyen descenso de gradiente estocástico (SGD), Adam y RMSProp. Esta habilidad se mide en esta prueba para evaluar la familiaridad con diferentes algoritmos de optimización y su impacto en la convergencia y el rendimiento de la red.
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 Prueba de aprendizaje profundo to be based on.
Neurona
Descenso de gradiente
Red neuronal de avance
Inclinación
Función de activación
Inicialización de peso
Exagerado
Regularización
Función de pérdida
Tasa de aprendizaje
Normalización por lotes
Abandonar
Capa convolucional
Agrupación
Red neuronal recurrente
LSTM
Ganancia
Modelado de idiomas
Incrustaciones de palabras
Arquitectura de CNN
Clasificación de imágenes
Detección de objetos
Segmentación de imagen
Arquitectura RNN
Reconocimiento de voz
Análisis de los sentimientos
Aprendizaje reforzado
Generación de texto
Algoritmos de optimización
Adam Optimizer
Descenso de gradiente estocástico
Decadencia de la tasa de aprendizaje
Transferir técnicas de aprendizaje
Modelos previos a la aparición
Arquitectura automática
Reducción de dimensionalidad
Codificador
Ajuste de hiperparameter
Aumento de datos
Autoencoders regularizados
Inyección de ruido
Problema de gradiente de desaparición
Modelos generativos
Entrenamiento GaN
Generación de imágenes
Ataques adversos
Interpretabilidad de CNN
Mecanismos de atención
Comprensión del lenguaje natural
Respuesta de preguntas visuales
Subtitulación de imágenes
Transformadores
Bert
Aprendizaje de refuerzo profundo
Gradiente de políticas
Valor iteración
Q-Learning
Autoencoders para la detección de anomalías
Redes neuronales artificiales

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

Científico de datos
Ingeniero de aprendizaje automático
Investigador de inteligencia artificial
Ingeniero de aprendizaje profundo
Analista de datos
Ingeniero de visión por computadora
Ingeniero de procesamiento de lenguaje natural
Consultor de IA
Roles de inteligencia artificial
Investigador científico

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

Conocimiento de autoencoders y sus aplicaciones
Competencia en algoritmos de optimización para redes neuronales
Capacidad para implementar descenso de gradiente y sus variantes
Comprensión del descenso de gradiente estocástico y sus variantes
Conocimiento de técnicas de programación de tarifas de aprendizaje
Competencia en la normalización por lotes en redes neuronales
Capacidad para implementar la regularización de deserción en los modelos
Comprensión de las estrategias de inicialización de peso
Conocimiento de la parada temprana en la capacitación de redes neuronales
Competencia en la evaluación del modelo y las técnicas de validación

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Ver cuadro de mando de muestra

Los gerentes de contratación sintieron que a través de las preguntas técnicas que hicieron durante las entrevistas del panel, pudieron decir qué candidatos tenían mejores puntajes y diferenciarse de aquellos que no obtuvieron tan buenos puntajes. Ellos son altamente satisfecho con la calidad de los candidatos preseleccionados con la selección de Adaface.

85%

Reducción en el tiempo de detección

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Deep Learning Hiring Test Preguntas frecuentes

¿Puedo combinar múltiples habilidades en una evaluación personalizada?

Si, absolutamente. Las evaluaciones personalizadas se configuran en función de la descripción de su trabajo e incluirán preguntas sobre todas las habilidades imprescindibles que especifique.

¿Tiene alguna característica anti-trato o procuración en su lugar?

Tenemos las siguientes características anti-trate en su lugar:

Preguntas no postradas
Procuración de IP
Procedor web
Procedores de cámara web
Detección de plagio
navegador seguro

Lea más sobre las funciones de procuración.

¿Cómo interpreto los puntajes de las pruebas?

Lo principal a tener en cuenta es que una evaluación es una herramienta de eliminación, no una herramienta de selección. Una evaluación de habilidades está optimizada para ayudarlo a eliminar a los candidatos que no están técnicamente calificados para el rol, no está optimizado para ayudarlo a encontrar el mejor candidato para el papel. Por lo tanto, la forma ideal de usar una evaluación es decidir un puntaje umbral (generalmente del 55%, lo ayudamos a comparar) e invitar a todos los candidatos que obtienen un puntaje por encima del umbral para las próximas rondas de la entrevista.

¿Para qué nivel de experiencia puedo usar esta prueba?

Cada evaluación de AdaFace está personalizada para su descripción de trabajo/ persona candidata ideal (nuestros expertos en la materia elegirán las preguntas correctas para su evaluación de nuestra biblioteca de más de 10000 preguntas). Esta evaluación se puede personalizar para cualquier nivel de experiencia.

¿Cada candidato tiene las mismas preguntas?

Sí, te hace mucho más fácil comparar los candidatos. Las opciones para las preguntas de MCQ y el orden de las preguntas son aleatorizados. Tenemos características anti-trato/procuración en su lugar. En nuestro plan empresarial, también tenemos la opción de crear múltiples versiones de la misma evaluación con cuestiones de niveles de dificultad similares.