Explanation
This course is designed to provide a thorough hands-on experience in applying Deep Learning techniques to 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 will 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 Semantic Segmentation models in Python and PyTorch. The course covers the complete pipeline with hands-on experience of Semantic Segmentation using deep learning in Python and PyTorch At the end of this course, you will have the knowledge and skills you need to start deep learning problems. Semantic Segmentation on 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 start this exciting journey of Deep Learning with Semantic Component in Python and PyTorch.
What will you learn?
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Learn Semantic Segmentation for Complete Pipelines and Real-World Applications in Python & PyTorch using Google Colab
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Deep Learning Framework for Semantic Category (UNet, DeepLabV3, PSPNet, PAN, Unet++, MTCNet etc.)
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Data Tools and Data Annotation for Semantic Classification
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Data Extensions and Data Load Implementation in PyTorch
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Learn Performance Metrics (IOU, etc.) for Evaluating Distribution Models
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Transfer Learning and Pre-trained Deep Resnet Architecture
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Distribution Frameworks (UNet, PSPNet, DeepLab, PAN, UNet++) PyTorch Implementation Using Different Encoder and Decoder Architectures
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Learn how to optimize advanced parameters of segmentation models to improve performance during training
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Test the Trained Model and Calculate IOU, IOU-wise, Pixel Accuracy, Precision, Recall and F Score
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View Distribution Results and Create a Distribution Map of the Predicted RGB Output
Who is this course for?
- This course is designed for individuals interested in learning how to apply Deep Learning techniques to solve real-world Semantic Segmentation problems using the Python programming language and the PyTorch Deep Learning Framework.
- This course is intended for many 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
- Overall, the course is intended for anyone who wants to learn how to use Deep Learning to extract meaning from visual data and to gain a deeper understanding of the theory and practical applications of Semantic Segmentation using Python and PyTorch. .
A Deep Learning Guide to Image Segmentation in Python & Pytorch
Includes 2023/1
Deep Learning Requirements for Image Distribution in Python & Pytorch
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Deep learning of Semantic Segmentation with Python and Pytorch is taught in this course along the complete Zero to Hero pipeline.
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No prior knowledge of Semantic Category is assumed. Everything will be covered in hands-on training
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A Google Gmail account is required to start Google Colab to write Python Code
Pictures
Sample Clip
Installation Guide
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file size
1.26GB