Certainly, here are some precise resources for learning about Residual Networks (ResNets) up to a professional level: 1. **Research Paper**: - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385) - This is the original ResNet paper by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. It provides a comprehensive understanding of ResNets. 2. **Online Courses**: - [Coursera - Convolutional Neural Networks Specialization](https://www.coursera.org/specializations/convolutional-neural-networks) - This specialization by Andrew Ng includes a course on Structuring Machine Learning Projects that covers the concept of ResNets in the context of computer vision. 3. **YouTube Videos**: - [ResNets Explained - A Must-Read for Deep Learning Enthusiasts](https://www.youtube.com/watch?v=GWt6Fu05voI) - This video by Yann LeCun provides a concise explanation of ResNets. 4. **Medium Articles**: - [Understanding and Implementing Architectures of ResNet and ResNeXt for state-of-the-art Image Classification: From Microsoft in 2015 to Efficient Networks in 2019](https://towardsdatascience.com/understanding-and-implementation-of-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cc5d8d2cc3ff) - This Medium article provides an in-depth understanding of ResNets and their variations. 5. **Books**: - "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville - Chapter 7 of this book provides a comprehensive overview of convolutional networks, which includes ResNets. 6. **GitHub Repository**: - [Keras ResNet](https://github.com/keras-team/keras/blob/master/keras/applications/resnet50.py) - The official Keras repository provides the implementation of ResNet models, which can be a valuable resource for practical implementation. These resources should give you a solid foundation in understanding and implementing Residual Networks for various deep learning tasks, particularly in the context of computer vision.