Mastering Image Segmentation with PyTorch

Mastering Image Segmentation with PyTorch, available at $64.99, has an average rating of 3.2, with 45 lectures, based on 63 reviews, and has 432 subscribers.

You will learn about 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 This course is ideal for individuals who are Developers who want to understand and implement Image Segmentation or Data Scientists who want to broaden their scope of Deep Learning techniques It is particularly useful for Developers who want to understand and implement Image Segmentation or Data Scientists who want to broaden their scope of Deep Learning techniques.

Enroll now: Mastering Image Segmentation with PyTorch

Summary

Title: Mastering Image Segmentation with PyTorch

Price: $64.99

Average Rating: 3.2

Number of Lectures: 45

Number of Published Lectures: 45

Number of Curriculum Items: 45

Number of Published Curriculum Objects: 45

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

Developers who want to understand and implement Image Segmentation

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

Target Audiences

Developers who want to understand and implement Image Segmentation

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

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

Traina 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 Curriculum

Chapter 1: Course Overview & Setup

Lecture 1: Image Segmentation (101)

Lecture 2: Course Scope

Lecture 3: System Setup

Lecture 4: How to Get The Material

Lecture 5: Conda Environment Setup

Chapter 2: PyTorch Introduction (Refresher)

Lecture 1: PyTorch Introduction (101)

Lecture 2: From Tensors to Computational Graphs (101)

Lecture 3: Tensor (Coding)

Lecture 4: Linear Regression from Scratch (Coding, Model Training)

Lecture 5: Linear Regression from Scratch (Coding, Model Evaluation)

Lecture 6: Model Class (Coding)

Lecture 7: Exercise: Learning Rate and Number of Epochs

Lecture 8: Solution: Learning Rate and Number of Epochs

Lecture 9: Batches (101)

Lecture 10: Batches (Coding)

Lecture 11: Datasets and Dataloaders (101)

Lecture 12: Datasets and Dataloaders (Coding)

Lecture 13: Saving and Loading Models (101)

Lecture 14: Saving and Loading Models (Coding)

Lecture 15: Model Training (101)

Lecture 16: Hyperparameter Tuning (101)

Lecture 17: Hyperparameter Tuning (Coding)

Chapter 3: Convolutional Neural Networks (Refresher)

Lecture 1: CNN Introduction (101)

Lecture 2: CNN (Interactive)

Lecture 3: Image Preprocessing (101)

Lecture 4: Image Preprocessing (Coding)

Lecture 5: Layer Calculations (101)

Lecture 6: Layer Calculations (Coding)

Chapter 4: Semantic Segmentation

Lecture 1: Architecture (101)

Lecture 2: Upsampling (101)

Lecture 3: Loss Functions (101)

Lecture 4: Evaluation Metrics (101)

Lecture 5: Coding Introduction (101)

Lecture 6: Data Prep Intro (101)

Lecture 7: Data Prep I: create folders (Coding)

Lecture 8: Data Prep II: patches function (Coding)

Lecture 9: Data Prep III: create all patch-images (Coding)

Lecture 10: Modeling: Dataset (Coding)

Lecture 11: Modeling: Model Setup (Coding)

Lecture 12: Modeling: Training Loop (Coding)

Lecture 13: Modeling: Losses and Saving (Coding)

Lecture 14: Model Evaluation: Calc Metrics (Coding)

Lecture 15: Model Evaluation: Check Prediction (Coding)

Chapter 5: Additional Information

Lecture 1: Closing Remarks

Lecture 2: Bonus Lecture

Instructors

Bert Gollnick
Data Scientist

Rating Distribution

1 stars: 2 votes

2 stars: 4 votes

3 stars: 7 votes

4 stars: 18 votes

5 stars: 32 votes

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