AkexStar/LiGe-DeepLearning-PKUCourse
Jupyter notebook 深度学习技术与应用（2023春-李戈老师课程）
This project provides Jupyter notebooks that guide users through practical deep learning tasks, using common datasets like MNIST, CIFAR, and SVHN. It allows you to experiment with different neural network architectures and training methodologies, such as multi-layer perceptrons for image classification, adversarial attacks on image models, and even code generation. The resource is designed for students or practitioners looking to deepen their understanding of deep learning concepts through hands-on exercises.
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Use this if you are a student or researcher in deep learning looking for practical, step-by-step guidance on implementing and experimenting with core deep learning algorithms and models.
Not ideal if you are looking for a plug-and-play solution for immediate application or a highly optimized, production-ready deep learning library.
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May 16, 2024
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