Course Syllabus 

• Convolution: Understand the fundamental concept of convolution and its role in extracting meaningful features from images. Explore various filter operations and learn to apply convolutions effectively to uncover valuable patterns.
• Max Pooling: Delve into the concept of max pooling, a technique used to downsample feature maps and capture dominant features. Gain proficiency in incorporating max pooling layers within CNN architectures to enhance model performance.
• Convolutional Networks: Learn about the architecture and design principles of convolutional networks. Examine the different layers involved, such as convolutional layers, pooling layers, and fully connected layers. Grasp the significance of each layer and its impact on network performance.
• Training your Model with a GPU: Discover the advantages of leveraging GPUs for training CNNs. Learn to harness PyTorch's GPU capabilities to accelerate model training, optimize memory usage, and effectively manage GPU resources for enhanced performance.
• Pre-trained Networks: Uncover the power of pre-trained networks and transfer learning. Explore pre-trained CNN models like ResNet, VGG, and AlexNet, and gain insights into leveraging their knowledge for efficient solving of image analysis tasks.

Prerequisites

1. Completion of PyTorch: Tensor, Dataset and Data Augmentation course
2. Completion of Linear Regression with PyTorch course
3. Completion of Classification with PyTorch course
4. Completion of Build a Neural Network with PyTorch course