• T.Naga Navya
• Bolla Susmitha
• Saripudi Sri Tirisha
• Naredla Pallavi
• Sadhu Naga Meenashi

Recently, deep learning has been used in picture categorization, word detection and recognition, object tracking, posture estimation, scene labeling, and visual saliency detection. Models like Auto Encoder, Sparse Coding, Restricted Boltzmann Machine, Deep Belief Networks, and Convolutional Neural Networks are frequently used in deep learning. In comparison to previous models, convolutional neural networks have demonstrated strong performance in picture classification. In this paper, a simple convolutional neural network for image classification was constructed. This simple convolutional neural network completed the image classification. We also looked at a number of learning rate setting strategies and other optimization algorithms built on top of the Convolutional neural network to find the optimal parameters that have the biggest impact on image classification.

Deep learning, Convolutional Neural Network (CNN), image classification, MINIST Image Datasets, Machine Learning.

"Classifying Images Using ConvolutionalNeural Networks (CNNS)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 11, page no.548-554, November-2024, Available :http://www.jetir.org/papers/JETIRGO06059.pdf

"Classifying Images Using ConvolutionalNeural Networks (CNNS)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 11, page no. pp548-554, November-2024, Available at : http://www.jetir.org/papers/JETIRGO06059.pdf