Udemy – Deep Learning for Image Segmentation with Python & Pytorch 2022-12

This course is designed to provide a comprehensive, hands-on experience in applying Deep Learning techniques to Semantic Image Segmentation problems. Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? In this course, you’ll learn how to use the power of Deep Learning to segment images and extract meaning from visual data. You’ll start with an introduction to the basics of Semantic Segmentation using Deep Learning, then move on to implementing and training your own models for Semantic Segmentation with Python and PyTorch. The course covers the complete pipeline with hands-on experience of Semantic Segmentation using Deep Learning with Python and PyTorch By the end of this course, you’ll have the knowledge and skills you need to start applying Deep Learning to Semantic Segmentation problems in your own work or research. Whether you’re a Computer Vision Engineer, Data Scientist, or Developer, this course is the perfect way to take your understanding of Deep Learning to the next level. Let’s get started on this exciting journey of Deep Learning for Semantic Segmentation with Python and PyTorch.

What you’ll learn

• Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch using Google Colab
• Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.)
• Datasets and Data annotations Tool for Semantic Segmentation
• Data Augmentation and Data Loaders Implementation in PyTorch
• Learn Performance Metrics (IOU, etc.) for Segmentation Models Evaluation
• Transfer Learning and Pretrained Deep Resnet Architecture
• Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) Implementation in PyTorch using different Encoder and Decoder Architectures
• Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training
• Test Segmentation Trained Model and Calculate IOU, Class-wise IOU, Pixel Accuracy, Precision, Recall and F-score
• Visualize Segmentation Results and Generate RGB Predicted Output Segmentation Map

Who this course is for

• This course is designed for individuals who are interested in learning how to apply Deep Learning techniques to solve Semantic Segmentation problems in real-world using the Python programming language and the PyTorch Deep Learning Framework
• This course is designed for a wide range of Students and Professionals, including but not limited to: Machine Learning Engineers, Deep Learning Engineers, Data Scientists, Computer Vision Engineers, and Researchers who want to learn how to use PyTorch to build and train deep learning models for Semantic Segmentation
• In general, the course is for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch

Specificatoin of Deep Learning for Image Segmentation with Python & Pytorch

• Publisher : Udemy
• Teacher : Mazhar Hussain , Computer Science & AI School
• Language : English
• Level : All levels
• Number of Course : 23
• Duration : 3 hours and 6 minutes

Content on 2023/1

Requirements of Deep Learning for Image Segmentation with Python & Pytorch

• Deep Learning for Semantic Segmentation with Python and Pytorch is taught in this course by following a complete pipeline from Zero to Hero
• No prior knowledge of Semantic Segmentation is assumed. Everything will be covered with hands-on training
• A Google Gmail account is required to get started with Google Colab to write Python Code

Pictures

Sample Clip

Installation Guide

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Download Links

Download Part 1 – 1 GB

Download Part 2 – 269 MB

File size

1.26 GB