The Factors Those are Influencing Performance and Accuracy in Case of Conventional Deep Learning Approach

• Gunjan Arya

  Author

• Dr Narender Kumar

  Author

There are many technical and data-driven factors that affect how well and accurately Deep Learning models work. These factors are especially important in traditional deep learning methods. This study looks at the main factors that affect how well the model works and how well it can predict the future by using a lot of secondary data from books, papers, and other academic sources. The main goal of the study is to find out how things like data quality, model architecture, hyperparameter tuning, training processes, and computing resources affect how well the model works overall. looks at well-known topologies, like Convolutional Neural Networks and Recurrent Neural Networks, to find out how the design of the structure and the way the network learns affect its accuracy. It shows that good datasets with clear labels make model results much better, while bad or uneven data can cause estimates to be biased and less generalization. It has also been found that learning rate, batch size, number of layers, and optimization methods are very important in determining model stability and convergence. This article talks about how computational power, like GPUs and parallel processing, can speed up training and make it easier to work with big amounts of data. Problems like too much or too little fit, as well as not being able to be interpreted, are also talked about as big problems that lower performance. Regularization techniques, dropout methods, and data addition have all been found to be good ways to deal with these problems.

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