Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models

• URL: http://arxiv.org/abs/2409.13566v2
• Date: Wed, 11 Dec 2024 04:40:00 GMT
• Title: Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models
• Authors: Keyu Chen, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Pohsun Feng,
• Abstract summary: The study covers modern architectures, including ResNet, MobileNet, and EfficientNet. A comparison of linear probing and model fine-tuning is presented, supplemented by visualizations using techniques like PCA, t-SNE, and UMAP.
• Score: 17.372501468675303
• License:
• Abstract: 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.

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