Analysing Digits with Sequential Convolutional Neural Networks and Adam Optimizer

Numerical analysis is the focus of this study, which employs the Adam optimizer in conjunction with CNNs. Computer vision and deep learning have enabled exact number recognition, which is crucial for many current applications such as optical character recognition and automated document processing. The chosen Sequential CNN design is perfect for efficient feature representation and extraction of basic services because of its pooling and convolutional layers. The Adam optimizer's ability to adjust the learning rate makes model training more efficient and speeds up convergence. Improving the model's accuracy is the main focus of the study. This may be achieved by methods such as regularisation, data augmentation, and hyperparameter tuning. Using assessment tools such as confusion matrices and classification reports, you may find out everything about the model's strengths and weaknesses. The model's capacity to produce and adapt to new numerical data sets is proven as it undergoes training, evaluation, and optimisation. The positive outcomes achieved by integrating a well-designed Sequential CNN with the Adam optimizer demonstrate the method's applicability to digit identification issues. This paper contributes to the ongoing discussion in computer vision and machine learning by exploring the complexities of digit analysis utilising Sequential CNNs and the Adam optimizer. The study does more than just establish a social solid foundation for accurate number identification; it also paves the way for future advancements in the broader area of AI applications as it shows a perfect precision.