Convolutional Neural Networks with PyTorch

In this course you will gain practical skills to tackle real-world image analysis and computer vision challenges using PyTorch. Uncover the power of Convolutional Neural Networks (CNNs) and explore the fundamentals of convolution, max pooling, and convolutional networks. Learn to train your models with GPUs and leverage pre-trained networks for transfer learning. . Note, this course is a part of a PyTorch Learning Path, check Prerequisites Section.

Course Syllabus
Throughout the course, participants will dive deep into key topics and gain hands-on experience to master CNNs. The curriculum covers the following essential areas:
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
Note: this course is a part of PyTorch Learning Path and the following is required:
Completion of PyTorch: Tensor, Dataset and Data Augmentation course
Completion of Linear Regression with PyTorch course
Completion of Classification with PyTorch course
Completion of Build a Neural Network with PyTorch course
or a good understanding of PyTorch Tensors and DataSets, Linear Regression and Classification, Neural Networks Principles.