What you'll learn
implement multi-class semantic segmentation with PyTorch on a real-world dataset
get familiar with different architectures like UNet, FPN
understand theoretical background, e.g. on upsampling, loss functions, evaluation metrics
perform data preparation to reshape inputs to appropriate format

Requirements
Basic Python knowledge

Description
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
Who this course is for:
Developers who want to understand and implement Image Segmentation
Data Scientists who want to broaden their scope of Deep Learning techniques