Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This repo is a deep dive into the details of deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 21-chapter course, learners will learn to implement and train their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. Additionally, the self-build package L0CV and running-live Jupyter Notebook will give them the opportunity to train and apply multi-million parameter networks on real-world vision problems of their choice. Through multiple hands-on tasks and the elementary research project, learners will acquire the toolset for setting up deep learning tasks and practical engineering tricks for training and fine-tuning deep neural networks.

*This illustration comes from d2l.ai

L0CV-Universe

如果你也是从这里出发，在开源的项目中应用进去，并在标题下给出引用 ，您的项目将会在这里展现！

*The design of this page is inspired by cs231n.stanford.edu