Machine learning paradigm and application area of deep learning and types of neural network

IF 1.5 Q2 EDUCATION & EDUCATIONAL RESEARCH
Y. Yaswanth, Samala Rohan, Neerudu Anusha, Gali Mohith
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引用次数: 0

Abstract

Machine learning and deep learning are rapidly evolving paradigms that are being used to solve a wide range of problems in various application areas. This literature review summary highlights the different types of neural networks and tools used in machine learning, as well as the use cases for deep learning, such as image identification, natural language processing, audio recognition, anomaly detection, and recommender systems. The existing system of machine learning is constantly expanding, with new techniques and architectures being developed to address real world problems. A proposed system for machine learning involves identifying the problem, collecting and preparing data, selecting and training appropriate models, evaluating performance, deploying and monitoring the model, and continuously updating it to improve its accuracy and effectiveness.
机器学习的范例和应用领域的深度学习和神经网络的类型
机器学习和深度学习是快速发展的范式,正被用于解决各种应用领域的广泛问题。这篇文献综述总结了机器学习中使用的不同类型的神经网络和工具,以及深度学习的用例,如图像识别、自然语言处理、音频识别、异常检测和推荐系统。现有的机器学习系统正在不断扩展,新的技术和架构正在开发,以解决现实世界的问题。一个提议的机器学习系统包括识别问题、收集和准备数据、选择和训练适当的模型、评估性能、部署和监控模型,以及不断更新模型以提高其准确性和有效性。
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来源期刊
CiteScore
4.20
自引率
10.00%
发文量
26
期刊介绍: IJICTE publishes contributions from all disciplines of information technology education. In particular, the journal supports multidisciplinary research in the following areas: •Acceptable use policies and fair use laws •Administrative applications of information technology education •Corporate information technology training •Data-driven decision making and strategic technology planning •Educational/ training software evaluation •Effective planning, marketing, management and leadership of technology education •Impact of technology in society and related equity issues
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