Summary of Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models, by Keyu Chen et al.
Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models
by Keyu Chen, Ziqian Bi, Qian Niu, Junyu Liu, Benji Peng, Sen Zhang, Ming Liu, Ming Li, Xuanhe Pan, Jiawei Xu, Jinlang Wang, Pohsun Feng
First submitted to arxiv on: 20 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study explores the application of TensorFlow pre-trained models in deep learning, focusing on practical guidance for tasks like image classification and object detection. It covers modern architectures such as 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, along with visualizations using techniques like PCA, t-SNE, and UMAP. The paper provides complete example code and step-by-step instructions, offering valuable insights for both beginners and advanced users. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how to use pre-trained models in deep learning. It gives practical tips on how to do tasks like image classification and object detection using TensorFlow. The study covers the latest architectures like ResNet, MobileNet, and EfficientNet, and shows how transfer learning works with real-world examples. It also compares linear probing and model fine-tuning, making it easy to understand what these approaches do. |
Keywords
» Artificial intelligence » Deep learning » Fine tuning » Image classification » Object detection » Pca » Resnet » Transfer learning » Umap
