Course Information
Course Overview
Master the art of image segmentation with PyTorch with hands-on training and real-world projects
Welcome to "Mastering Image Segmentation with PyTorch"! In this course, you will learn everything you need to know to get started with image segmentation using PyTorch.
Image segmentation is a key technology in the field of computer vision, which enables computers to understand the content of an image at a pixel level. It has numerous applications, including autonomous vehicles, medical imaging, and augmented reality.
This course is designed for both beginners and experts in the field of computer vision. If you are a beginner, we will start with the basics of PyTorch and how to use it for simple modeling. Then, you will learn how to implement popular semantic segmentation models such as FPN or U-Net.
By the end of this course, you will have the skills and knowledge to tackle real-world semantic segmentation projects using PyTorch.
So why wait? Join me today and take the first step towards mastering image segmentation with PyTorch!
In my course I will teach you:
Tensor handling
creation and specific features of tensors
automatic gradient calculation (autograd)
Modeling introduction, incl.
Linear Regression from scratch
understanding PyTorch model training
Batches
Datasets and Dataloaders
Hyperparameter Tuning
saving and loading models
Convolutional Neural Networks
CNN theory
layer dimension calculation
image transformations
Semantic Segmentation
Architecture
Upsampling
Loss Functions
Evaluation Metrics
Train a Semantic Segmentation Model on a custom Dataset
Enroll right now to learn some of the coolest techniques and boost your career with your new skills.
Best regards,
Bert
Course Content
- 5 section(s)
- 45 lecture(s)
- Section 1 Course Overview & Setup
- Section 2 PyTorch Introduction (Refresher)
- Section 3 Convolutional Neural Networks (Refresher)
- Section 4 Semantic Segmentation
- Section 5 Additional Information
What You’ll Learn
- implement multi-class semantic segmentation with PyTorch on a real-world dataset
- get familiar with architectures like UNet, FPN
- understand theoretical background, e.g. on upsampling, loss functions, evaluation metrics
- perform data preparation to reshape inputs to appropriate format
Skills covered in this course
Reviews
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KKevin Chou
EXTREMELY practical course. Exactly what I was looking for. Be warned, this course is best taken with prior knowledge in pytorch, deep learning, CNN. Nonetheless, it is so practical that it shows how exactly to do things, and I am confident in implementing my own code after the class, which I cannot say for many other courses on this website.
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JJon Sauer
3 years ML medical images
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CChâu Lê
I skipped all the refresher sessions since I'm not a beginner. I started this course from the 'Semantic segmentation' session. The way the teacher delivered the theory makes it easy to understand. However, in the coding video, he could have done better to explain why we need to manipulate the shape of some tensors. Overall, good course on semantic segmentation.
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LLauren Fyle
No complaints. easy to understand. Clear expectations