Modern Computer Vision & Deep Learning with Python & PyTorch (Udemy.com)

Welcome to the course "Modern Computer Vision & Deep Learning with Python & PyTorch"! Imagine being able to teach computers to see just like humans. Computer Vision is a type of artificial intelligence (AI) that enables computers and machines to see the visual world, just like the way humans see and understand their environment. Artificial intelligence (AI) enables computers to think, where Computer Vision enables computers to see, observe and interpret. This course is particularly designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to major Computer Vision problems including Image Classification, Semantic Segmentation, Instance Segmentation, and Object Detection. In this course, you'll start with an introduction to the basics of Computer Vision and Deep Learning, and learn how to implement, train, test, evaluate and deploy your own models using Python and PyTorch for Image Classification, Image Segmentation, and Object Detection.

Computer Vision plays a vital role in the development of autonomous vehicles. It enables the vehicle to perceive and understand its surroundings to detect and classify various objects in the environment, such as pedestrians, vehicles, traffic signs, and obstacles. This helps to make informed decisions for safe and efficient vehicle navigation. Computer Vision is used for Surveillance and Security using drones to track suspicious activities, intruders, and objects of interest. It enables real-time monitoring and threat detection in public spaces, airports, banks, and other security-sensitive areas. Today Computer Vision applications in our daily life are very common including Face Detection in cameras and cell phones, logging in to devices with fingerprints and face recognition, interactive games, MRI, CT scans, image guided surgery and much more. This comprehensive course is especially designed to give you hands-on experience using Python and Pytorch coding to build, train, test and deploy your own models for major Computer Vision problems including Image Classification, Image Segmentation (Semantic Segmentation and Instance Segmentation), and Object Detection. So, are you ready to unleash the power of Computer Vision and Deep Learning with Python and PyTorch:

• Master the cutting-edge techniques and algorithms driving the field of Computer Vision.

• Dive deep into the world of Deep Learning and gain hands-on experience with Python and PyTorch, the industry-leading framework.

• Discover the secrets behind building intelligent systems that can understand, interpret, and make decisions from visual data.

• Unlock the power to revolutionize industries such as healthcare, autonomous systems, robotics, and more.

• Gain practical skills through immersive projects, real-world applications, and hands-on coding exercises.

• Gain insights into best practices, industry trends, and future directions in computer vision and deep learning.

What You'll Learn:

This course covers the complete pipeline with hands-on experience of Computer Vision tasks using Deep Learning with Python and PyTorch as follows:

• Introduction to Computer Vision and Deep Learning with real-world applications

• Learn Deep Convolutional Neural Networks (CNN) for Computer Vision

• You will use Google Colab Notebooks for writing the python and Pytorch code.

• Perform two types of Image Classification using Deep Learning models with Python.

  • Single-label Classification.

  • Multi-label Classification.

• You will be able to learn Transfer Learning techniques:

  • Transfer Learning by FineTuning the Model.

  • Transfer Learning by using the Model as Fixed Feature Extractor.

• You will learn how to perform Data Augmentation.

• You will Learn to FineTune the Deep Resnet Model.

• You will learn how to use the Deep Resnet Model as Fixed Feature Extractor.

• You will Learn HyperParameters Optimization and results visualization.

• Semantic Image Segmentation and its Real-World Applications in Self Driving Cars or Autonomous Vehicles etc.

• Deep Learning Architectures for Semantic Segmentation including:

  • UNet, and UNet++

  • Pyramid Scene Parsing Network (PSPNet),

  • Pyramid Attention Network (PAN), 

  • Multi-Task Contextual Network (MTCNet),

  • DeepLabV3, etc.

  • YOLO26

• Datasets and Data annotations Tool for Semantic Segmentation

• Data Augmentation and Data Loading in PyTorch for Semantic Segmentation

• Performance Metrics (IOU) for Segmentation Models Evaluation

• Segmentation Models Implementation in PyTorch using different Encoder and Decoder Architectures

• Hyperparameters Optimization and Training of Segmentation Models

• Test Segmentation Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score

• Visualize Segmentation Results and Generate RGB Predicted Segmentation Map

• Learn Object Detection using Deep Learning Models with Pytorch

• Learn Object Detection Deep Learning Architecture:

  • YOLO8

  • YOLO11

  • YOLO26

  • RCNN,

  • Fast RCNN,

  • Faster RCNN

  • Mask RCNN

• Perform Object Detection with Fast RCNN and Faster RCNN

• Introduction to Detectron2 by Facebook AI Research (FAIR)

• Preform Object Detection with Detectron2 Models

• Explore Custom Object Detection Dataset with Annotations

• Perform Object Detection on Custom Dataset using Deep Learning

• Train, Test, Evaluate Your Own Object Detection Models and Visualize Results

• Perform Instance Segmentation using Mask RCNN on Custom Dataset with Pytorch and Python

Who Should Attend:

This course is designed for a wide range of students and professionals, including but not limited to:

• Computer Vision Engineers, Artificial Intelligence AI enthusiasts and Researchers who want to learn how to use Python adn PyTorch to build, train and deploy Deep Learning models for Computer Vision problems

• Machine Learning Engineers, Deep Learning Engineers, and Data Scientists who want to apply Deep Learning to Computer Vision tasks

• Developers who want to incorporate Computer Vision and Deep Learning capabilities into their projects