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Erschienen in:
Deep Learning Assisted Diagnosis of Parkinson’s Disease Kapitel lesen Erstes Kapitel lesen

2024 | OriginalPaper | Buchkapitel

Deep Learning: How to Apply Machine Learning and Deep Learning Methods to Audio Analysis

verfasst von : Manan Dabral, Tejinder Kaur, Abhay Khanna, Ashish Yadav, Ojas Sharma, Nakul

Erschienen in: Mobile Radio Communications and 5G Networks

Verlag: Springer Nature Singapore

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Abstract

So before understanding about deep learning, we should also look at Artificial Intelligence (AI) and Machine Learning (ML). The purpose of AI is to train machines in such a way that they can function like the human mind. The field of AI includes machine learning, the purpose of which is that the machine can learn by itself according to its experience and can develop such skills in which human involvement is not equal. Let us now understand what Deep Learning is. You can also say that very complex neural networks have been named deep learning, and you can also see it as an advancement in machine learning. Basic machine learning had limited data processing capabilities and generally required structured data. While the data processing capacity of deep learning algorithm is very high, and compared to traditional machine learning, it does not require structured data, rather it can handle both structured and unstructured data. In one sentence, deep learning enables computers to think, understand, and experience like humans.

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Vorheriges Kapitel Deep Learning Assisted Diagnosis of Parkinson’s Disease
Nächstes Kapitel Naive Bayes Classifier-Based Smishing Detection Framework to Reduce Cyber Attack
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Metadaten
Titel
Deep Learning: How to Apply Machine Learning and Deep Learning Methods to Audio Analysis
verfasst von
Manan Dabral
Tejinder Kaur
Abhay Khanna
Ashish Yadav
Ojas Sharma
Nakul
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
Buch
Mobile Radio Communications and 5G Networks

Print ISBN: 978-981-9706-99-0

Electronic ISBN: 978-981-9707-00-3

Copyright-Jahr: 2024

DOI
https://doi.org/10.1007/978-981-97-0700-3_2