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[Application] Creating a PyTorch Deep Learning Model: Utilizing Artificial Neural Networks

This is a practical course where you will learn how to actually build computer vision and deep learning models using PyTorch. Many learners understand deep learning theory but struggle with the actual process of implementing and training models. In this course, we start with the basic concepts of image processing and move on to understanding and directly implementing key deep learning models such as CNN, LeNet, AlexNet, VGGNet, Inception, and ResNet. Furthermore, you will learn the latest computer vision technologies, including Object Detection, Semantic Segmentation, Instance Segmentation, and GAN, through hands-on practice. Through this course, learners will acquire the practical skills to build and train real-world deep learning models using PyTorch.

1 learners are taking this course

Level Intermediate

Course period Unlimited

PyTorch
PyTorch
Deep Learning(DL)
Deep Learning(DL)
CNN
CNN
Python
Python
PyTorch
PyTorch
Deep Learning(DL)
Deep Learning(DL)
CNN
CNN
Python
Python

What you will gain after the course

  • Ability to directly build and train deep learning models using PyTorch

  • Ability to understand the architecture and operating principles of CNN-based computer vision models

  • Ability to implement the latest deep learning technologies such as Object Detection, Segmentation, and GAN

  • Experience in conducting deep learning projects using real-world datasets

Building Computer Vision Deep Learning Models Using PyTorch: Learning Deep Learning through Practice, from CNN to Object Detection, Segmentation, and GANs

In this course, you will learn how to actually implement various deep learning models used in the field of computer vision using PyTorch. It covers core technologies used in real-world AI projects, such as image classification, object detection, and image segmentation, and explores the structures and principles of CNN-based models like LeNet, AlexNet, VGGNet, Inception, and ResNet. Additionally, you will learn the latest computer vision techniques, including Object Detection, Semantic Segmentation, Instance Segmentation, and GANs, through hands-on practice. Through this course, learners will acquire the ability to implement deep learning models that can be applied to real projects, not just theory.

What you will learn


Section (1) Key Keywords

In this course, you will understand the core concepts of computer vision and deep learning, and learn how to directly implement various deep learning models using PyTorch.

First, you will understand the basic concepts of computer vision and image data processing methods. Afterwards, you will learn the structure and operating principles of Convolutional Neural Networks (CNNs) and explore the development process of deep learning models by implementing representative CNN models such as LeNet, AlexNet, and VGGNet.

In the next step, you will learn about advanced CNN architectures such as Inception (GoogLeNet) and ResNet and understand how complex deep learning models improve performance.

Additionally, you will learn Object Detection techniques and practice how to detect specific objects within images. Subsequently, you will learn techniques for analyzing and partitioning each region of an image through Semantic Segmentation and Instance Segmentation.

Finally, you will understand the basic principles of generative deep learning models that create new images through GAN (Generative Adversarial Network) and implement them through hands-on practice.

Through this course, learners will study various deep learning techniques used in the field of computer vision through hands-on practice and develop the skills to apply them to real-world projects.

Notes Before Taking the Course

Learning Materials

This course provides various materials to assist with your learning.

  • Python and PyTorch-based practice source code

  • Dataset examples used in the practice sessions

  • Text and reference materials summarizing the lecture content

  • Hands-on project example code

All practice materials are provided with the lectures, allowing learners to implement deep learning models by running them directly.
It is recommended to conduct the practice in an environment where Python and PyTorch are installed.

Recommended for
these people

Who is this course right for?

  • Developers who want to learn computer vision deep learning using PyTorch

  • AI/Data Science learners who want to actually implement a deep learning model (CNN)

  • Students and researchers who want to create their own computer vision projects

  • Python developers who want to understand deep learning theory through hands-on practice

Need to know before starting?

  • Python Basic Syntax

  • Basic concepts of deep learning or machine learning

  • Basic data processing experience

Hello
This is itgo4790

ITGO Co., Ltd., which operates the IT e-learning specialized site ITGO established in 2001, is a content production company that produces and distributes IT e-learning content.

ITGO produces courses by inviting practical experts and teaching professionals from various IT fields, and we continuously produce and supply about 150 new courses annually to keep pace with the constantly evolving and changing nature of the IT industry.

In addition, we actively collect learners' opinions and reflect them as much as possible when planning and opening new courses.  

All of our employees will continue to strive to recruit excellent instructors and stay updated on the latest trends in the IT field to produce high-quality courses.

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Curriculum

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27 lectures ∙ (11hr 42min)

Course Materials:

Lecture resources
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