Fundamentals of Computer Vision with PyTorch

This repository contains a Jupyter Notebook that guides you through the fundamentals of computer vision using the PyTorch framework. The notebook is structured to provide a hands-on approach to learning deep learning concepts, implementing neural networks, and applying them to various computer vision tasks. This notebook is to be used in conjuction with the video course.

Modules Overview

Module 2: Deep Learning with PyTorch

• Implementation of basic neural networks.
• Operations on tensors, including reshaping, slicing, and mathematical operations.
• Utilization of GPU for tensor operations if available.

Module 3: Solving Computer Vision Problems

• Advanced techniques such as convolutional neural networks (CNNs), object detection, semantic segmentation, and pose estimation.
• Practical applications using pre-trained models like YOLOv5 for object detection and DeepLabV3 for semantic segmentation..

Module 5: MLOps

• Integration with Data Version Control Data Version Control
• Monitoring with Weights & Biases Weights and Biases

Created by Richmond Alake