# Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models Source: [Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models]() Collection index: [llms.txt]() Data updated: 2025-11-21T05:01:56.111Z Authors: Keyu Chen, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Xinyuan Song, Zekun Jiang, Tianyang Wang, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Pohsun Feng Published: 2024-09-20T15:07:14.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()