Question
How is an artificial neural network related to machine learning?
A. An artificial neural network uses preprogrammed responses instead of learning.
B. An artificial neural network is a machine learning technique.
C. An artificial neural network is an earlier form of machine learning.
D. An artificial neural network does not require programming like a machine learning system.
Answer
B. An artificial neural network is a machine learning technique.
Explanation 1
The correct answer to the question is B. An artificial neural network is a machine learning technique.
An artificial neural network (ANN) is a computational model that is inspired by the structure and function of biological neurons. An ANN consists of a collection of interconnected units called artificial neurons, which can process information and learn from data. An ANN can be trained to perform various tasks, such as classification, regression, clustering, pattern recognition, natural language processing, computer vision, and more.
Machine learning (ML) is a branch of artificial intelligence that focuses on creating systems that can learn from data and improve their performance without explicit programming. ML algorithms can be divided into two main categories: supervised learning and unsupervised learning. Supervised learning algorithms learn from labeled data, while unsupervised learning algorithms learn from unlabeled data.
An ANN is a machine learning technique because it belongs to the supervised learning category. An ANN can learn from labeled data by adjusting its weights and biases through a process called backpropagation, which minimizes the error between the actual output and the desired output. An ANN can also learn from unlabeled data by using techniques such as autoencoders, which are ANNs that try to reconstruct their input as their output.
Therefore, an ANN is related to machine learning because it is one of the methods that can be used to create systems that can learn from data and improve their performance without explicit programming.
Explanation 2
The answer is B. An artificial neural network is a machine learning technique.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
An artificial neural network (ANN) is a type of machine learning algorithm that is inspired by the human brain. ANNs are made up of interconnected nodes, or neurons, that learn to recognize patterns in data. They are used in a wide variety of applications, including image recognition, natural language processing, and speech recognition.
In other words, ANNs are a type of machine learning technique that uses algorithms inspired by the human brain to learn from data and make predictions.
The other answer choices are incorrect.
- A. An artificial neural network uses preprogrammed responses instead of learning. This is incorrect. ANNs do not use preprogrammed responses. They learn from data to make predictions.
- C. An artificial neural network is an earlier form of machine learning. This is also incorrect. ANNs are a type of machine learning, but they are not the earliest form of machine learning. Other machine learning techniques, such as decision trees and support vector machines, existed before ANNs.
- D. An artificial neural network does not require programming like a machine learning system. This is also incorrect. ANNs do require programming. The programmer must specify the architecture of the ANN, the learning algorithm, and the training data.
Therefore, the answer is B. An artificial neural network is a machine learning technique.
Explanation 3
The correct answer is B. An artificial neural network is a machine learning technique.
An artificial neural network (ANN) is inspired by the biological brain’s neural network, designed to simulate the learning process. It is a fundamental component of machine learning (ML), a field of artificial intelligence (AI) that allows computers to learn from and make decisions without explicit programming.
In detail, a neural network is composed of layers of ‘neurons’ or ‘nodes,’ which can take in input, process it (using a set of weights that are adjusted during the learning process), and produce an output. Information is passed through these layers to solve tasks like image recognition, speech recognition, or even complex challenges in natural language processing.
Machine learning techniques, such as artificial neural networks, are developed to improve their accuracy or ‘learn’ over time as they are exposed to more data. This ‘learning’ happens during a training phase where the model adjusts its internal weights based on the error of its predictions using an algorithm like gradient descent.
Unlike choice A and D, an artificial neural network depends on learning and does require a form of programming to set up the learning process. As for choice C, rather than being an earlier form of machine learning, ANN is a subset of machine learning techniques and is in active use, demonstrating cutting-edge performance on many tasks.
Explanation 4
The answer is B. An artificial neural network is a machine learning technique.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Artificial neural networks (ANNs) are a type of machine learning algorithm that are inspired by the human brain. ANNs are made up of interconnected nodes, similar to the neurons in the brain. These nodes are able to learn from data and make predictions based on that data.
In other words, ANNs are a type of machine learning algorithm that use a neural network to learn from data.
The other answer choices are incorrect.
- A. An artificial neural network uses preprogrammed responses instead of learning. This is incorrect. ANNs do not use preprogrammed responses. They learn from data.
- C. An artificial neural network is an earlier form of machine learning. This is also incorrect. ANNs are a type of machine learning, but they are not the earliest form of machine learning.
- D. An artificial neural network does not require programming like a machine learning system. This is also incorrect. ANNs do require programming. The programming is what tells the ANN how to learn from data.
Therefore, the answer is B. An artificial neural network is a machine learning technique.
Explanation 5
The correct answer is B. An artificial neural network is a machine learning technique.
An artificial neural network (ANN) is a subset of machine learning that is inspired by the structure and function of the human brain. It consists of a network of interconnected nodes or artificial neurons that can process data and learn from it. An ANN can perform tasks such as prediction, classification, clustering, and optimization by adjusting its weights and biases based on the input data and the desired output.
Machine learning is a broader field of artificial intelligence that encompasses various techniques and algorithms that can learn from data and improve their performance without explicit programming. Machine learning can be divided into three main types: supervised learning, unsupervised learning, and reinforcement learning. ANN is one of the many techniques that can be used for supervised or unsupervised learning.
Therefore, an ANN is not an earlier form of machine learning, nor does it use preprogrammed responses or avoid programming. It is a specific technique within machine learning that mimics the biological neural networks.
Explanation 6
The answer is B. An artificial neural network is a machine learning technique.
Machine learning is a type of artificial intelligence (AI) that allows software applications to become more accurate in predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values.
Artificial neural networks (ANNs) are a type of machine learning algorithm that are inspired by the human brain. ANNs are made up of interconnected nodes, similar to the neurons in the brain. These nodes are able to learn from data and make predictions.
In other words, ANNs are a type of machine learning algorithm that can learn from data and make predictions.
The other answer choices are incorrect.
- A. An artificial neural network uses preprogrammed responses instead of learning. This is not true. ANNs learn from data, not from preprogrammed responses.
- C. An artificial neural network is an earlier form of machine learning. This is also not true. ANNs are a type of machine learning, but they are not the earliest form of machine learning.
- D. An artificial neural network does not require programming like a machine learning system. This is also not true. ANNs do require programming, just like any other machine learning system.
Therefore, the answer is B. An artificial neural network is a machine learning technique.
Explanation 7
An artificial neural network is a machine learning technique. Artificial Neural Networks (ANNs) are modeled after the human brain and are able to learn from data and provide responses in the form of predictions or classifications. ANNs are used for a variety of tasks such as image recognition, speech recognition, machine translation, and medical diagnosis. ANNs are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold. ANNs are just one of many tools and approaches used in machine learning algorithms.
Therefore, the correct answer is B. An artificial neural network is a machine learning technique.
Explanation 8
The correct answer is B. An artificial neural network is a machine learning technique.
Machine learning is a branch of artificial intelligence that involves creating systems that can learn from data and improve their performance over time. Machine learning techniques can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning.
An artificial neural network (ANN) is a machine learning technique that is inspired by the structure and function of the human brain. An ANN consists of a network of artificial neurons, or nodes, that are connected by weights and biases. Each node receives inputs from other nodes or external sources, processes them using an activation function, and produces an output that can be sent to other nodes or used as a final prediction or classification.
An ANN can learn from data by adjusting its weights and biases based on the error between the actual output and the desired output. This process is called training, and it can be done using various algorithms, such as gradient descent, backpropagation, or stochastic gradient descent.
An ANN can be used for various machine learning tasks, such as regression, classification, clustering, dimensionality reduction, anomaly detection, natural language processing, computer vision, speech recognition, and more.
Explanation 9
An artificial neural network is a machine learning technique. Artificial Neural Networks (ANNs) are a type of machine learning algorithm modeled after the human brain. ANNs are able to learn from data and provide responses in the form of predictions or classifications. ANNs are comprised of node layers, containing an input layer, one or more hidden layers, and an output layer. Each node connects to another and has an associated weight and threshold.
Therefore, the answer is B. An artificial neural network is a machine learning technique.
Explanation 10
The correct answer is B. An artificial neural network is a machine learning technique.
An artificial neural network (ANN) is a computational model inspired by the structure and functioning of biological neural networks. It consists of interconnected nodes, called artificial neurons or units, which process and transmit information. These nodes are organized in layers, with input nodes receiving data, hidden nodes performing computations, and output nodes providing the final result.
Machine learning, on the other hand, is a broader field that encompasses various techniques and algorithms that enable computers to learn from data and make predictions or decisions without being explicitly programmed. Machine learning algorithms learn patterns and relationships from the data through training, enabling the system to improve its performance over time.
Artificial neural networks are one of the fundamental techniques within the field of machine learning. They are particularly well-suited for tasks such as pattern recognition, classification, regression, and clustering. Neural networks learn from data by adjusting the weights and biases of the connections between neurons, optimizing the network’s ability to make accurate predictions or classifications.
Option A, “An artificial neural network uses preprogrammed responses instead of learning,” is incorrect. Neural networks do not rely on preprogrammed responses; instead, they learn from data through training.
Option C, “An artificial neural network is an earlier form of machine learning,” is also incorrect. Artificial neural networks are not an earlier form of machine learning but rather a specific technique within the broader field of machine learning. Machine learning encompasses a wide range of methods beyond neural networks.
Option D, “An artificial neural network does not require programming like a machine learning system,” is incorrect. Both artificial neural networks and machine learning systems require programming. While the training process of a neural network involves adjusting the weights and biases, it still requires programming to define the network architecture, implement the training algorithm, and handle the data flow.
In summary, an artificial neural network is a machine learning technique that uses interconnected nodes to process and transmit information, allowing the network to learn patterns and make predictions from data.
Reference
- What are Neural Networks? | IBM
- Artificial neural network – Wikipedia
- Artificial Neural Networks for Machine Learning – Every aspect you need to know about – DataFlair (data-flair.training)
- AI vs. Machine Learning vs. Deep Learning vs. Neural Networks: What’s the difference? (ibm.com)
- How MNCs benefited from AI/ML. Lets see how the companies are using… | by kore arvind | Medium
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