+44 330 818 7254Machine learning algorithms have gained significant attention in recent years due to their ability to extract meaningful patterns and insights from vast amounts of data. However, achieving optimal performance often requires a substantial amount of labeled data, which can be expensive and time-consuming to acquire. In this research article, we propose a novel approach to enhance machine learning performance by combining transfer learning and data augmentation techniques. Transfer learning leverages pre-trained models on large datasets to bootstrap the learning process on smaller, domain-specific datasets. By utilizing the knowledge learned from the source task, transfer learning can improve generalization and speed up convergence on the target task. Data augmentation, on the other hand, increases the size and diversity of the training dataset by applying various transformations such as rotation, translation, and scaling. This process helps the model learn robust representations and reduces over fitting. In this study, we conducted experiments on a benchmark dataset in the field of computer vision. We employed convolutional neural network architecture and compared the performance of three different scenarios: (1) a baseline model trained from scratch with limited labeled data, (2) transfer learning using a pretrained model without data augmentation, and (3) transfer learning with data augmentation. The results of our experiments demonstrate that combining transfer learning and data augmentation techniques leads to significant improvements in the model's performance. Compared to the baseline model, the transfer learning approach achieved higher accuracy with a smaller number of labeled samples. Furthermore, the introduction of data augmentation further enhanced the performance, leading to even better accuracy and improved generalization on unseen data. These findings highlight the importance of leveraging existing knowledge from pre-trained models and augmenting the training data to enhance machine learning performance. This research contributes to the growing body of knowledge on improving the efficiency and effectiveness of machine learning algorithms, particularly in scenarios with limited labeled data.
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