Mastering Image Segmentation with PyTorch

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Bert Gollnick

5:01:15

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1. Course Overview & Setup

1. Image Segmentation (101).mp4

07:41

2. Course Scope.mp4

03:46

3. System Setup.mp4

04:22

4. How to Get The Material.mp4

02:28

5. Conda Environment Setup.mp4

07:02

2. PyTorch Introduction

1. PyTorch Introduction (101).mp4

03:06

2. From Tensors to Computational Graphs (101).mp4

08:17

3. Tensor (Coding).mp4

13:11

4. Linear Regression from Scratch (Coding, Model Training).mp4

09:55

5. Linear Regression from Scratch (Coding, Model Evaluation).mp4

07:09

6. Model Class (Coding).mp4

14:05

7. Exercise Learning Rate and Number of Epochs.mp4

00:41

8. Solution Learning Rate and Number of Epochs.mp4

05:01

9. Batches (101).mp4

02:59

10. Batches (Coding).mp4

05:09

11. Datasets and Dataloaders (101).mp4

04:22

12. Datasets and Dataloaders (Coding).mp4

10:40

13. Saving and Loading Models (101).mp4

03:12

14. Saving and Loading Models (Coding).mp4

03:40

15. Model Training (101).mp4

06:27

16. Hyperparameter Tuning (101).mp4

09:17

17. Hyperparameter Tuning (Coding).mp4

07:55

3. Convolutional Neural Networks

1. CNN Introduction (101).mp4

10:04

2. CNN (Interactive).mp4

03:43

3. Image Preprocessing (101).mp4

08:38

4. Image Preprocessing (Coding).mp4

09:27

5. Layer Calculations (101).mp4

06:53

6. Layer Calculations (Coding).mp4

10:43

4. Semantic Segmentation

1. Architecture (101).mp4

07:29

2. Upsampling (101).mp4

06:13

3. Loss Functions (101).mp4

04:07

4. Evaluation Metrics (101).mp4

03:11

5. Coding Introduction (101).mp4

02:46

6. Data Prep Intro (101).mp4

02:41

7. Data Prep I create folders (Coding).mp4

06:26

8. Data Prep II patches function (Coding).mp4

10:36

9. Data Prep III create all patch-images (Coding).mp4

10:26

10. Modeling Dataset (Coding).mp4

14:52

11. Modeling Model Setup (Coding).mp4

07:57

12. Modeling Training Loop (Coding).mp4

08:54

13. Modeling Losses and Saving (Coding).mp4

03:40

14. Model Evaluation Calc Metrics (Coding).mp4

12:10

15. Model Evaluation Check Prediction (Coding).mp4

09:54

5. Additional Information

1. Bonus Lecture.html

Description

Master the art of image segmentation with PyTorch with hands-on training and real-world projects

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

Who is this for?

Developers who want to understand and implement Image Segmentation

Data Scientists who want to broaden their scope of Deep Learning techniques

More details

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

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I am a hands-on Data Scientist with a lot of domain knowledge on Renewable Energies, especially Wind Energy.Currently I work for a leading manufacturer of wind turbines. I provide trainings on Data Science and Machine Learning with R and Python since many years.I studied Aeronautics, and Economics. My main interests are Machine Learning, Data Science, and Blockchain.

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