Deep Learning for Image Segmentation with Python & Pytorch

Deep Learning for Image Segmentation with Python & Pytorch, available at $49.99, has an average rating of 4.17, with 37 lectures, based on 152 reviews, and has 593 subscribers.

You will learn about Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.) Segmentation with Pretrained Pytorch Models (FCN, DeepLabV3) on COCO Dataset Perform Image Segmentation with Deep Learning Models on Custom Datasets 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 Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) in PyTorch using different Encoder and Decoder Architectures Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training on Custom Dataset 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 This course is ideal for individuals who are 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 or 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 or 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 It is particularly useful 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 or 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 or 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.

Enroll now: Deep Learning for Image Segmentation with Python & Pytorch

Summary

Title: Deep Learning for Image Segmentation with Python & Pytorch

Price: $49.99

Average Rating: 4.17

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $74.99

Quality Status: approved

Status: Live

What You Will Learn

• Learn Image Semantic Segmentation Complete Pipeline and its Real-world Applications with Python & PyTorch
• Deep Learning Architectures for Semantic Segmentation (UNet, DeepLabV3, PSPNet, PAN, UNet++, MTCNet etc.)
• Segmentation with Pretrained Pytorch Models (FCN, DeepLabV3) on COCO Dataset
• Perform Image Segmentation with Deep Learning Models on Custom Datasets
• 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
• Implement Segmentation Models (UNet, PSPNet, DeepLab, PAN, UNet++) in PyTorch using different Encoder and Decoder Architectures
• Learn to Optimize Hyperparameters for Segmentation Models to Improve the Performance during Training on Custom Dataset
• 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 Should Attend

• 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

Target Audiences

• 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