Deep Learning Test?

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| By Livyn

Livyn

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Quizzes Created: 304 | Total Attempts: 170,029

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1/10 Questions

Deep learning is also known as what?
- Deep structured learning
- Soft structure learning
- Deep ladder learning
- Ladder learning

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.

2.

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.

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3.

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
4.

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.

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5.

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.

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1
6.

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.

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7.

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.

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8.

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.

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9.

What is DNN?
- Dropped neural networks
- Direct neural networks
- Deep neural networks
- Deeper neural networks
Correct Answer

A. Deep neural networks

Explanation

Deep neural networks (DNN) is the correct answer. DNN refers to a type of artificial neural network that has multiple hidden layers between the input and output layers. These hidden layers allow the network to learn complex patterns and representations, making it capable of handling large amounts of data and solving complex problems. The term "deep" in DNN refers to the depth of these hidden layers, which distinguishes them from shallow neural networks that have fewer hidden layers.

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10.

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.

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