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Deep Learning Assessment Test

This Deep Learning test evaluates candidates on key skills such as Neural Network Fundamentals, RNN, Transfer Learning, Optimization Algorithms, Model Deployment, CNN, Hyperparameter Tuning, Regularization Techniques, Dynamic programming, TensorFlow and PyTorch, and DNN.

Proficiency Level
Beginner-Expert
Experience
0-8 years
Duration
40 mins
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Use Case

  • Evaluates understanding of neural networks and model functionalities.
  • Assesses proficiency in advanced deep learning architectures and strategies.
  • Identifies expertise in optimization, tuning, and deployment techniques.
  • Tests ability to implement efficient solutions in dynamic programming scenarios.

Skills Covered

Neural Network Fundamentals
Deep Neural Networks (DNN)
Convolutional Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Transfer Learning
TensorFlow and PyTorch
Optimization Algorithms
+4 more

About

Deep Learning Assessment Test

This Deep Learning test is designed to assess candidates' proficiency in essential areas of deep learning, including Neural Network Fundamentals, Recurrent Neural Networks (RNN), Transfer Learning, Optimization Algorithms, Model Deployment, Convolutional Neural Networks (CNN), Hyperparameter Tuning, Regularization Techniques, Dynamic programming, TensorFlow and PyTorch, and Deep Neural Networks (DNN). The test aims to identify individuals who possess a solid understanding of these concepts and can apply them effectively in real-world scenarios. It is an ideal tool for evaluating potential hires for roles that require a strong foundation in deep learning technologies and methodologies.

Target Audience

This assessment is suitable for roles such as Deep Learning Engineer, Machine Learning Engineer, and AI Engineer.

Prerequisites
  • Understanding of basic machine learning concepts
  • Familiarity with Python programming language
  • Knowledge of linear algebra and calculus
  • Experience with TensorFlow or PyTorch
  • Basic understanding of neural network architectures
  • Familiarity with optimization techniques
  • Understanding of data preprocessing and augmentation
Test Overview
Duration
40 mins
Questions
11
Passing Score
70%

Questions

Understanding Activation Functions in Neural Networks
Neural Network Fundamentals
Neural Network Fundamentals
Machine Learning
Activation Functions
What this question evaluates
This question assesses the candidate's understanding of neural networks and the role of activation functions in adding non-linearity to the model for learning complex patterns.
Type:
Programming
Difficulty:
Easy
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Dropout in DNNs
Deep Neural Networks
Deep Neural Networks
Dropout
Regularization
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Convolutional Layers in CNN
Convolutional Neural Networks
Convolutional Neural Networks
Deep Learning
Machine Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Memory in RNNs
RNN
RNN
LSTM
Sequence Prediction
Deep Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Transfer Learning
transfer learning
transfer learning
deep learning
machine learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Medium
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Dynamic vs Static Computation Graphs in TensorFlow and PyTorch
TensorFlow
TensorFlow
PyTorch
Computation Graphs
Machine Learning
Deep Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Adam Optimization
optimization algorithms
optimization algorithms
adam
machine learning
deep learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Hyperparameter Tuning Strategy
Hyperparameter Tuning
Hyperparameter Tuning
Optimization
Machine Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Understanding Regularization Techniques in Machine Learning
Regularization
Regularization
Overfitting
Machine Learning
Deep Learning
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Machine Learning Model Deployment Strategies
Machine Learning
Machine Learning
Model Deployment
Production
What this question evaluates
No description provided
Type:
Programming
Difficulty:
Hard
Time:
2 mins
Attempts:
100+
Success Rate:
70.01%
Creating teams
Optimization
Optimization
Problem Solving
Skill Allocation
What this question evaluates
This question assesses the candidate's skills in team formation, skill allocation, and optimization of expert numbers in a company setting.
Type:
Programming
Difficulty:
Medium
Time:
30 mins
Attempts:
100+
Success Rate:
70.01%
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Interactive coding environment with real-time feedback
Clear instructions and test cases for each question
Built-in code editor with syntax highlighting
Immediate evaluation of submissions
Progress tracking throughout the assessment
Detailed explanations for correct answers
Time management tools to help pace yourself

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Dashboard mockup
Sofia Rossi
Candidate
Passed
85%
AI Summary
Skills Performance
Score
Neural Network Fundamentals
87%
Deep Neural Networks (DNN)
80%
Convolutional Neural Networks (CNN)
85%
Recurrent Neural Networks (RNN)
82%
Areas of Improvement
Review
Recurrent Neural Networks (RNN)
Practice
Deep Neural Networks (DNN)
Skill Assessment
Detailed evaluation of technical skills and problem-solving abilities.
AI Analysis
Machine learning-powered insights into candidate performance patterns.
Benchmarking
Compare results against industry standards and other candidates.
Action Items
Specific recommendations for skill development and improvement.

Pricing Plans

Freemium
US$ 0
5 credits / mo
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Skill fit candidates
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Conduct face to face interviews
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Standard compliance, security and audits
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Standard support from WeCP Team
Premium
US$ 240
40 credits / mo
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Add-on credit at $8/credit
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Comprehensive Assessment Capabilities
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Advance Skill Analytics
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Learning & Development Integration
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Employee Friendly User Experience
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Retention-Focused Features
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Advance compliance, security and audits
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Proactive support from WeCP Team
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Access to WeCP AI Copilot to save cost, time and improve outcomes
Enterprise
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Candidate Skills
US$ 100
10 credits / mo + 1 seat
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Add-on seat at $8 /seat
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Add-on credit at $10 /credit
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Use Prebuilt Questions
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Standard Cheat Prevention
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Smooth Candidate Experience
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Skill fit candidates
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Conduct face to face interviews
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ATS Integrations
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Standard compliance, security and audits
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Standard support from WeCP Team
Freemium
US$ 0
60 credits / yr
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Use Prebuilt Questions
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Standard Cheat Prevention
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Smooth Candidate Experience
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Skill fit candidates
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Conduct face to face interviews
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ATS Integrations
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Standard compliance, security and audits
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Standard support from WeCP Team
Premium
US$ 2388
500 credits / yr
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Add-on credit at $8 /credit
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Comprehensive Assessment Capabilities
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Advance Skill Analytics
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Learning & Development Integration
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Employee Friendly User Experience
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Retention-Focused Features
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Advance compliance, security and audits
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Proactive support from WeCP Team
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Access to WeCP AI Copilot to save cost, time and improve outcomes
Enterprise
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