Lecture 09. CNNs - clairedavid/ml_in_hep GitHub Wiki
The lecture format
Learn how to learn with video.
- Guest speaker.
Outline (extract keywords and put them in intro)
- Definition of CNNs: A brief explanation of what CNNs are and how they differ from traditional neural networks.
- Motivation for using CNNs: A discussion of why CNNs are particularly well-suited for tasks involving images, such as image classification, object detection, and segmentation.
- Overview of CNN architecture: A high-level description of the main components of a CNN, including the convolutional layers, pooling layers, and fully connected layers.
- Convolutional layers: A detailed explanation of how convolutional layers work, including the concept of convolutional filters and stride.
- Pooling layers: A description of the purpose and function of pooling layers in a CNN, including the two main types of pooling (max pooling and average pooling).
- Fully connected layers: A discussion of the role of fully connected layers in a CNN, and how they are used to make final predictions based on the output of the convolutional and pooling layers.
- Applications of CNNs: Examples of real-world tasks that CNNs have been successfully applied to, such as image classification, object detection, and segmentation.
List of Videos
- "Convolutional Neural Networks (CNNs) - The Math of Intelligence (Week 4)" by Siraj Raval: This video provides a concise and intuitive explanation of CNNs, including the concept of convolutional filters and stride, and how CNNs are used for image classification.
- "Convolutional Neural Networks (CNNs) - Deep Learning with Neural Networks and TensorFlow" by Sentdex: This video covers the basics of CNNs, as well as more advanced topics such as transfer learning and fine-tuning.
- "Convolutional Neural Networks (CNNs) Explained" by Data School: This video provides a thorough overview of CNNs, including the different types of layers and how they work together to process images and make predictions.
- "Convolutional Neural Networks (CNNs) for Computer Vision" by Computer Science: This video covers the basics of CNNs, as well as more advanced topics such as object detection and segmentation.
These are just a few examples, and there are many other good videos available online that explain CNNs in different ways. It may be helpful to watch a few different videos to get a well-rounded understanding of this topic.