Understanding Deep Learning is a classic textbook in the field of deep learning, written by Simon J.D. Prince and published by MIT Press on December 5, 2023. This book covers many key concepts in the field of deep learning, suitable for beginners and experienced developers to read. This repository provides the Chinese translation of the book, which is machine-translated using ChatGPT and reviewed by humans to ensure accuracy.

Understanding Deep Learning is a book that delves into the principles and applications of deep learning. It provides a wealth of mathematical background knowledge, supervised learning, the construction and training of neural networks, and comprehensive content in the field of deep learning. The Python notebooks provided in the book help readers deepen their understanding through practice. In addition, there are resources for teachers, including images, slides, and teaching materials.

This project is a comprehensive guide to using open-source large models, covering environment setup, model deployment, and efficient fine-tuning. It simplifies the use and application of open-source large models, enabling more ordinary learners to access and utilize them. The project is targeted towards learners interested in open-source large models and who want to get hands-on experience. It provides detailed instructions on environment configuration, model deployment, and fine-tuning methods.

Understanding Deep Learning is a classic textbook in the field of deep learning, written by Simon J.D. Prince and published by MIT Press on December 5, 2023. This book covers many key concepts in the field of deep learning, suitable for beginners and experienced developers to read. This repository provides the Chinese translation of the book, which is machine-translated using ChatGPT and reviewed by humans to ensure accuracy.

Understanding Deep Learning is a book that delves into the principles and applications of deep learning. It provides a wealth of mathematical background knowledge, supervised learning, the construction and training of neural networks, and comprehensive content in the field of deep learning. The Python notebooks provided in the book help readers deepen their understanding through practice. In addition, there are resources for teachers, including images, slides, and teaching materials.

This project is a comprehensive guide to using open-source large models, covering environment setup, model deployment, and efficient fine-tuning. It simplifies the use and application of open-source large models, enabling more ordinary learners to access and utilize them. The project is targeted towards learners interested in open-source large models and who want to get hands-on experience. It provides detailed instructions on environment configuration, model deployment, and fine-tuning methods.