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

• URL: http://arxiv.org/abs/2409.13566v1
• Date: Fri, 20 Sep 2024 15:07:14 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 book covers practical implementations of modern architectures like ResNet, MobileNet, and EfficientNet. It compares linear probing and model fine-tuning, offering visualizations using techniques such as PCA, t-SNE, and UMAP. By blending theoretical insights with hands-on practice, this book equips readers with the knowledge to confidently tackle various deep learning challenges.
• Score: 17.372501468675303
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This book focuses on the application of TensorFlow pre-trained models in deep learning, providing detailed guidance on effectively using these models for tasks such as image classification and object detection. It covers practical implementations of modern architectures like ResNet, MobileNet, and EfficientNet, demonstrating the power of transfer learning through real-world examples and experiments. The book compares linear probing and model fine-tuning, offering visualizations using techniques such as PCA, t-SNE, and UMAP to help readers intuitively understand the impact of different approaches. Designed for beginners to advanced users, this book includes complete example code and step-by-step instructions, enabling readers to quickly master how to leverage pre-trained models to improve performance in practical scenarios. By blending theoretical insights with hands-on practice, this book equips readers with the knowledge to confidently tackle various deep learning challenges.

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