IMAGE CLASSIFICATION WITH STOCHASTIC GRADIENT DESCENT METHOD
As one of the branches of Artificial Intelligence, Machine Learning is actively developing in various fields of technology and human life. With the increasing popularity of big data, large-scale Machine Learning problems have emerged that must be solved. One way is to use a mathematical model. Ma...
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| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/84282 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | As one of the branches of Artificial Intelligence, Machine Learning is actively developing
in various fields of technology and human life. With the increasing popularity of big
data, large-scale Machine Learning problems have emerged that must be solved. One
way is to use a mathematical model. Many forms of mathematical models are built to
solve cases in the Machine Learning domain, one of which is the problem of image
classification. In this study, the focus will be on solving image classification problems
using a perceptron model or simple neural network model. In building a model, the
model that is to be built is an optimal one with good accuracy and high computational
efficiency. In practice, the model is built using the gradient method. The main focus
of this research is the influence of stochastic elements in the Gradient Descent method
on model accuracy and computational efficiency. The experimental results show that
overall computational efficiency can be higher when combining stochastic elements
in the gradient descent method with certain stopping criteria. Model accuracy is also
influenced by adding stochastic elements to the gradient descent method where the
increase or decrease in model accuracy is influenced by the batch size chosen. |
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