This Artificial Neural Network test evaluates candidates' proficiency in training and optimizing neural networks, hyperparameter tuning, data preprocessing techniques, neural network architecture, data structures and algorithms, and frameworks like TensorFlow, Keras, and PyTorch.
This Artificial Neural Network test is designed to assess candidates' expertise in training and optimizing neural networks, hyperparameter tuning, data preprocessing techniques, and neural network architecture. It also evaluates knowledge of data structures and algorithms, and proficiency in using popular frameworks such as TensorFlow, Keras, and PyTorch. Additionally, the test covers interpretability and explainability methods, ensuring candidates can effectively communicate their model's decisions. Ideal for roles like Deep Learning Engineer and Computer Vision Engineer, this assessment helps identify individuals with a strong foundation in neural network principles and practical application skills.
Deep Learning Engineer, Computer Vision Engineer
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Access to Objective Way of Assessments - MCQs, Checboxes, Fill in the blanks, Subjective (Manually Scored), File Upload (Manually Scored)
Access to Practice Way of Assessments - Programming, Debugging, Database, Data Science,
Access to Real-world, work like Assessments - Projects, DevOps, Machine Learning, Subjective (AI Evaluated), Video (Technical Knowledge), Video (Communication Skill), Whiteboarding (AI Evaluated)
Assessment Design consultation services according to Curriculum, Training or Learning Paths