Deep Learning Test?

Reviewed by Editorial Team
The ProProfs editorial team is comprised of experienced subject matter experts. They've collectively created over 10,000 quizzes and lessons, serving over 100 million users. Our team includes in-house content moderators and subject matter experts, as well as a global network of rigorously trained contributors. All adhere to our comprehensive editorial guidelines, ensuring the delivery of high-quality content.
Learn about Our Editorial Process
| By Livyn
L
Livyn
Community Contributor
Quizzes Created: 304 | Total Attempts: 172,184
| Attempts: 1,014
SettingsSettings
Please wait...
  • 1/10 Questions

    What is DNN?

    • Dropped neural networks
    • Direct neural networks
    • Deep neural networks
    • Deeper neural networks
Please wait...
About This Quiz

Deep learning is part of a bigger family of machine learning. And I have for you some questions (10 to be specific) to solve. Do try your best.

Deep Learning Test? - Quiz

Quiz Preview

  • 2. 
    Deep learning is also known as what? - ProProfs

    Deep learning is also known as what?

    • Deep structured learning

    • Soft structure learning

    • Deep ladder learning

    • Ladder learning

    Correct Answer
    A. Deep structured learning
    Explanation
    Deep learning is also known as deep structured learning because it involves training neural networks with multiple layers to learn hierarchical representations of data. These deep structures allow the network to automatically extract features and patterns from the input data, enabling it to make more accurate predictions or classifications. The term "deep" refers to the depth of the network, which is characterized by the number of hidden layers it has. Therefore, deep structured learning is a more descriptive and accurate term for deep learning.

    Rate this question:

  • 3. 
    Still, what's another name for deep learning is? - ProProfs

    Still, what's another name for deep learning is?

    • V-Shape learning

    • Rope learning

    • Hierarchical learning

    • Loop learning

    Correct Answer
    A. Hierarchical learning
    Explanation
    Deep learning is often referred to as hierarchical learning because it involves multiple layers of artificial neural networks that process and learn representations of data in a hierarchical manner. Each layer of the network extracts increasingly complex features from the input data, allowing for the learning of intricate patterns and relationships. This hierarchical structure is one of the key characteristics of deep learning algorithms.

    Rate this question:

  • 4. 
    When was deep learning introduced to the community? - ProProfs

    When was deep learning introduced to the community?

    • 1978

    • 1986

    • 2002

    • 1995

    Correct Answer
    A. 1986
    Explanation
    Deep learning was introduced to the community in 1986.

    Rate this question:

  • 5. 
    When did Igor Aizenberg introduce it to ANN? - ProProfs

    When did Igor Aizenberg introduce it to ANN?

    • 1999

    • 2000

    • 2001

    • 2002

    Correct Answer
    A. 2000
    Explanation
    Igor Aizenberg introduced it to ANN in the year 2000.

    Rate this question:

  • 6. 
    Who introduced deep learning to Artificial Neural Networks? - ProProfs

    Who introduced deep learning to Artificial Neural Networks?

    • Igor Aizenberg

    • Frank Aizenberg

    • William Aizenberg

    • Piotr Aizenberg

    Correct Answer
    A. Igor Aizenberg
    Explanation
    Igor Aizenberg is the correct answer because he introduced deep learning to Artificial Neural Networks.

    Rate this question:

  • 7. 
    One of the following isn't an application of deep learning? - ProProfs

    One of the following isn't an application of deep learning?

    • Biological image processing

    • Visual art processing

    • Natural language processing

    • Image recognition

    Correct Answer
    A. Biological image processing
    Explanation
    Deep learning is a subfield of machine learning that focuses on training artificial neural networks to learn and make predictions. It is used in various applications such as visual art processing, natural language processing, and image recognition. However, biological image processing is not an application of deep learning. This is because biological image processing involves analyzing and interpreting images obtained from biological systems, such as microscopes, and does not typically involve training neural networks to make predictions.

    Rate this question:

  • 8. 
    What's the full definition of CAP? - ProProfs

    What's the full definition of CAP?

    • Credit assignment path

    • Coding assignment path

    • Cadre assignment path

    • Combinational assignment path

    Correct Answer
    A. Credit assignment path
    Explanation
    The full definition of CAP is Credit assignment path. This term refers to the process of assigning credit or responsibility for a particular outcome or result. In the context of machine learning and artificial intelligence, CAP is often used to determine how much credit should be given to each component or factor that contributes to the final outcome. It helps in understanding the contribution of different variables or features in the overall prediction or decision-making process.

    Rate this question:

  • 9. 
    Who coined the game? - ProProfs

    Who coined the game?

    • Kathy Dechter

    • Linda Dechter

    • Diana Dechter

    • Rina Dechter

    Correct Answer
    A. Rina Dechter
    Explanation
    Rina Dechter is the person who coined the game.

    Rate this question:

  • 10. 
    One of tge the following is true about deep learning? - ProProfs

    One of tge the following is true about deep learning?

    • Learn multiple levels of representations that correspond to different levels of abstraction; the levels form a hierarchy of concepts.

    • Use a rope-like layer that can be bounced from processing unit to another

    • Learn in supervised (e.g., classification) and/or unsupervised (e.g., pattern analysis) manners.

    • Use a cascade of multiple layers of nonlinear processing units for feature extraction and transformation.

    Correct Answer
    A. Use a rope-like layer that can be bounced from processing unit to another

Quiz Review Timeline (Updated): Mar 22, 2023 +

Our quizzes are rigorously reviewed, monitored and continuously updated by our expert board to maintain accuracy, relevance, and timeliness.

  • Current Version
  • Mar 22, 2023
    Quiz Edited by
    ProProfs Editorial Team
  • Mar 12, 2018
    Quiz Created by
    Livyn
Back to Top Back to top
Advertisement
×

Wait!
Here's an interesting quiz for you.

We have other quizzes matching your interest.