Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models
This work provides practical guidance and tools for users tackling deep learning challenges, but it is incremental as it focuses on applying existing methods.
The paper explores the application of TensorFlow pre-trained models for tasks like image classification and object detection, demonstrating the effectiveness of transfer learning through real-world examples and comparisons of linear probing and fine-tuning.
The application of TensorFlow pre-trained models in deep learning is explored, with an emphasis on practical guidance for tasks such as image classification and object detection. The study covers modern architectures, including ResNet, MobileNet, and EfficientNet, and demonstrates the effectiveness of transfer learning through real-world examples and experiments. A comparison of linear probing and model fine-tuning is presented, supplemented by visualizations using techniques like PCA, t-SNE, and UMAP, allowing for an intuitive understanding of the impact of these approaches. The work provides complete example code and step-by-step instructions, offering valuable insights for both beginners and advanced users. By integrating theoretical concepts with hands-on practice, the paper equips readers with the tools necessary to address deep learning challenges efficiently.