What is artificial neural network in machine learning in Hindi?
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| Papers (10) | Insight |
|---|---|
11 Citations | These interconnections of learning automata could be regarded as artificial neural networks. |
01 Apr 2016 22 Citations | In this paper, it's the first time we are presenting ASR designs based on two important machine learning paradigms Artificial Neural Network (ANN) and Support Vector Machine (SVM) for an rare and geographically important Indian dialect `Chhattisgarhi'. |
| Artificial Neural Network (ANN), a field of Artificial Intelligence (AI), is a popular way to identify unknown and hidden patterns in data which is suitable for share market prediction. | |
| Artificial neural network (ANN) is probably the most successful learning machine technique with flexible mathematical structure which is capable of identifying complex non-linear relationships between input and output data without attempting to reach the understanding of the nature of the phenomena. | |
| Artificial neural network is a versatile modelling tool capable of making predictions of values that are difficult to obtain in numerical analysis. | |
| The artificial neural network (ANN) is a computational method based on human brain function and is efficient in recognizing previously trained patterns. | |
87 Citations | The paper describes a neural network-based script identification system which can be used in the machine reading of documents written in English, Hindi and Kannada language scripts. |
01 Feb 2018 | At the same time, artificial neural network is a machine learning system, and it will get better results with the progress of training. |
3 Citations | This is the first implementation of machine comprehension models in code-mixed Hindi language using deep neural networks. |
| In this paper we propose an OCR for printed Hindi text in Devanagari script, using Artificial Neural Network (ANN), which improves its efficiency. |
Related Questions
What is an artificial neural network?4 answersAn Artificial Neural Network (ANN) is an information processing paradigm inspired by biological nervous systems, like the brain, designed to solve problems by mimicking the behavior of neurons and synapses. ANNs consist of interconnected processing elements (neurons) that learn from examples, similar to humans, through a training process for specific applications such as pattern recognition or data classification. These networks aim to implement a simplified model of the human brain, where neurons communicate with each other through synapses to process information and learn tasks. ANNs play a crucial role in machine learning, supporting supervised learning, unsupervised learning, and reinforcement learning, with the backpropagation algorithm being a key training method for neural networks.
What is artificial neuro network?5 answersAn Artificial Neural Network (ANN) is an information processing paradigm inspired by the way biological nervous systems, such as the brain, process information. It consists of interconnected processing elements called neurons that work together to solve specific problems. ANNs learn from examples and can be trained for tasks like pattern recognition and data classification. The structure of ANNs is designed to mimic the behavior of the human brain, with neurons communicating through connections called synapses. The power of ANNs lies in their ability to process information in parallel and learn from large datasets. They have applications in modeling complex patterns, prediction problems, and intelligent control systems. Deep learning, a type of ANN, processes data in complex patterns and can automate tasks that require human brain-like processing.
What is artificial neural networks?5 answersArtificial neural networks (ANNs) are computational methods that aim to mimic the behavior of the human brain. ANNs consist of interconnected neurons that communicate with each other through synapses. The power of ANNs lies in their ability to learn tasks by approximating any computable function. They have been successfully applied in various fields such as computer vision, robotics, speech recognition, and natural language processing. ANNs have shown excellent aptitude in learning the relationships between input and output data without prior assumptions about the statistical distribution. However, ANNs have limitations in terms of explainability and global approximation functions. Therefore, there is a need for research in novel approaches for local approximators that can outperform ANNs in terms of accuracy, computational expense, and explainability.
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