This course aims to teach learners the fundamentals of Convolutional Neural Networks (CNNs) and how to implement them for computer vision tasks using Python and PyTorch. By the end of the course, students will be able to understand the structure of CNNs, preprocess images for CNN input, build CNN models using PyTorch, train CNNs, and use them for inference. The course is designed for individuals interested in deep learning, computer vision, and image processing, with a focus on hands-on implementation using Python and PyTorch.
Overview
Syllabus
Intro
What Makes a Convolutional Neural Network
Image preprocessing for CNNs
Common components of a CNN
Components: pooling layers
Building the CNN with PyTorch
Notable CNNs
Implementation of CNNs
Image Preprocessing for CNNs
How to normalize images for CNN input
Image preprocessing pipeline with pytorch
Pytorch data loading pipeline for CNNs
Building the CNN with PyTorch
CNN training parameters
CNN training loop
Using PyTorch CNN for inference
Taught by
James Briggs
