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[Revised Edition] Deep Learning Computer Vision Complete Guide

This course will help you become a deep learning-based computer vision expert needed in the field through in-depth theoretical explanations of Object Detection and Segmentation, along with practical examples that can be immediately applied in real-world work environments.

(4.9) 163 reviews

3,990 learners

Level Intermediate

Course period Unlimited

  • dooleyz3525
Machine Learning(ML)
Machine Learning(ML)
Tensorflow
Tensorflow
Python
Python
Deep Learning(DL)
Deep Learning(DL)
Keras
Keras
Machine Learning(ML)
Machine Learning(ML)
Tensorflow
Tensorflow
Python
Python
Deep Learning(DL)
Deep Learning(DL)
Keras
Keras

Reviews from Early Learners

What you will gain after the course

  • Understanding Deep Learning-based Object Detection and Segmentation

  • In-depth theoretical learning of RCNN series, SSD, YOLO, RetinaNet, EfficientDet, and Mask RCNN

  • Learn how to use representative implementation packages for Object Detection and Segmentation such as MMDetection, Ultralytics Yolo, and AutoML EfficientDet

  • Performing Image/Video Object Detection/Segmentation using OpenCV and Tensorflow Hub

  • Learn various challenging practical examples to reach a level where you can directly apply Object Detection/Segmentation in real-world work

  • Acquiring diverse foundational knowledge that comprises Object Detection/Segmentation

  • Training Custom Datasets Using Various Implementation Packages and Creating Your Own Model

  • Gain hands-on experience with the advantages and disadvantages of various Object Detection/Segmentation models through practical examples

  • Working with major datasets like Pascal VOC and MS-COCO and converting them to TFRecord

  • Creating training data by applying annotations to datasets using the CVAT Tool

Lower the barrier, deepen the core!
Become a deep learning CNN practical expert.

Learn Deep Learning Computer Vision
with the latest revised edition.

Average rating 4.9★ chosen by 1,300+ students,
Inflearn Bestseller fully renewed in 2021!

Hello, this is Chulmin Kwon.
Thanks to the support of many people, I am pleased to release the revised edition of 'The Complete Guide to Deep Learning Computer Vision'.
About 90% of the videos from the existing course have been newly created, and I will introduce enhanced and additional content.

Based on the feedback you've provided for the course, we have created this revised edition with a focus on the following aspects.

  1. More detailed theoretical explanations for topics that received many student questions
  2. Object Detection/Segmentation package-based practice with the latest/best performance Object Detection/Segmentation có hiệu suất mới nhất/tốt nhất
  3. Reflecting the latest trends in Object Detection/Segmentation
  4. More flexible, diverse, and scalable practice code writing + more detailed explanations
  5. Various other additional lessons đa dạng khác

The revised course is without a doubt superior to the first edition, with more detailed content. It will guide you into the latest deep learning-based Object Detection and Segmentation domains.


Course Introduction 📝

The core of deep learning computer vision technology is rapidly shifting to Object Detection and Segmentation.

▲Intelligent video information recognition ▲AI vision inspection smart factories ▲Automated medical image diagnosis ▲Robotics ▲Autonomous vehicles, and many other fields are seeing the spread of deep learning-based Object Detection and Segmentation technologies. Accordingly, leading AI companies both domestically and internationally are sparing no investment in these fields and are seeking to secure development talent.

객체검출, 세그먼테이션 Two Major Trends Finally Meet: Object Detection & Segmentation

In recent years, as the fields of Object Detection and Segmentation have rapidly advanced, the demand for talent with relevant practical skills has been increasing. Nevertheless, as these are cutting-edge fields applying deep learning, there is a shortage of books, materials, and lectures for learning, making it difficult to properly train personnel.

We will guide you to become
a deep learning computer vision expert.

권 철민, 딥러닝 컴퓨터 비전 완벽 가이드

This course consists of in-depth theoretical explanations of Object Detection and Segmentation, along with numerous practical examples that can be directly applied in the field, and will help you grow into a deep learning-based computer vision expert needed in the industry.


From easy concept explanations
to in-depth theory.

We provide clear explanations of the vast Object Detection/Segmentation field, from easy concepts to in-depth theory on RCNN series, SSD, YOLO, RetinaNet, EfficientDet, Mask RCNN, and more.

객체검출, 세그먼테이션 You can thoroughly learn concepts with detailed lecture slides.

Maximize your practical deep learning skills
through hands-on examples.

There's no better way to improve your practical skills than by coding and implementing things yourself.
This course consists of many hands-on examples, which will maximize your practical ability to implement Object Detection and Segmentation.

몸이 기억하고 있다! ©SLAM DUNK

Recommended for
these people.

Those who have wondered how
deep learning CNN can be
applied in practice

Those who want to develop
deep learning-based
computer vision solutions

Those who want to expand from deep learning image classification capabilities
to the latest CV technologies

Graduate school applicants in AI,
job seekers/career changers
in deep learning-based CV field

Please check the prerequisites.

  • Python programming experience and basic understanding of deep learning CNN are required..
  • Additionally, it would be even better if you have some experience with TF.Keras or PyTorch.

Cutting-edge CV technology you won't find anywhere else,
all in one place.

Hands-on practice using
state-of-the-art Object Detection/Segmentation implementation
packages with exceptional performance

MMDetection, Ultralytics Yolo, AutoML EfficientDet, etc.
Hands-on inference practice using versatile OpenCV DNN and Tensorflow Hub

Object Detection/Segmentation practice on
various images and videos

Various real-world applications utilizing computer vision technology

Model Training Practice with
Various Custom Datasets

Various custom datasets

As a deep learning computer vision expert, you should be able to train models with various custom datasets to create your own Object Detection/Segmentation models. You should also be able to improve and evaluate the performance of these models.

This course will help you develop the ability to train custom datasets using various implementation packages and create optimal inference models.

Hands-on Custom Model Training / Inference
with Self-Created Training Datasets

Hands-on practice with self-created training datasets

Using the annotation tool CVAT, you will directly create a training dataset by applying bounding box annotations to regular images, and practice custom model training and inference using this created dataset.


Lab Environment 🧰

This course primarily conducts hands-on exercises based on GPU. For GPU-based exercises, the practice environment will be set up on Runpod, and for exercises unrelated to GPU, you may use the Google Colab environment.

For Runpod, approximately $10 to $20 in additional costs will be incurred for the hands-on practice. While you can proceed with the practice with $10 (though it's a bit tight ^^;;), we recommend around $20 for a more comfortable practice experience.

Please check before enrolling!

  • If you do not use Runpod's GPU environment, you may have difficulty following the examples. Thank you for your understanding in advance.

Practice Code and Course Materials 👨‍💻

Practice code can be downloaded from https://github.com/chulminkw/DLCV_New.Reviewing the practice code in advance will help you assess the prerequisite programming level needed to understand the exercises.

객체검출, 세그먼테이션 320-page lecture PDF textbook provided

The textbook used in the course (320 pages) can be downloaded from Lecture Section 0: Course Materials.


To learn theory,
there is no better way than hands-on practice.

Don't wait until you perfectly understand deep learning theory. There is no better way to learn theory than through practice.

Once you start coding, our brains are designed to follow along to gain concrete understanding. Try implementing the various practical examples presented in the lectures together with me. As you watch the lectures and actually implement them by typing on your keyboard, the parts that felt vague and abstract will gradually become tangible.

To become an expert, sometimes (though I think it's almost always the case) you need to run before you learn to walk. This course will be your best companion in developing your career and capabilities in the field of deep learning-based computer vision.

Thank you.

― Tony Stark's words to Jarvis during the Iron Man suit test in

"Sometimes you gotta run before you can walk."

People Inflearn Met 👨‍💻

Read the interview with Chulmin Kwon | Go read

Recommended for
these people

Who is this course right for?

  • Anyone interested in deep learning

  • Someone who has studied theory-focused learning on deep learning-based Object Detection and Segmentation

  • Someone who has been thinking about how deep learning CNN can be applied in practical work

  • Those who want to expand their capabilities beyond Deep Learning CNN Image Classification into Object Detection/Segmentation fields

  • Someone who wants to develop deep learning-based solutions in the Computer Vision field

  • Those who want to participate in Object Detection/Segmentation Challenges in competitions like Kaggle

  • Someone who is preparing for AI graduate school

  • Someone preparing to change careers to the Deep Learning-based Computer Vision field

Need to know before starting?

  • Python programming experience

  • Basic Understanding of Deep Learning CNN

  • (Optional) Basic experience with TF.Keras or Pytorch

Hello
This is

27,224

Learners

1,408

Reviews

4,024

Answers

4.9

Rating

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Curriculum

All

162 lectures ∙ (35hr 44min)

Course Materials:

Lecture resources
Published: 
Last updated: 

Reviews

All

163 reviews

4.9

163 reviews

  • sb0205185900님의 프로필 이미지
    sb0205185900

    Reviews 2

    Average Rating 3.0

    5

    52% enrolled

    It's really unbelievably good.. I was so mad at Infleun while listening to Coco Pytorch in the neighborhood. I found happiness after listening to this lecture. Thank you so much.

    • bskhan7801님의 프로필 이미지
      bskhan7801

      Reviews 15

      Average Rating 4.6

      5

      31% enrolled

      Professor Kwon Chul-min's lectures are always the best. Thank you.

      • yoonducklim0908님의 프로필 이미지
        yoonducklim0908

        Reviews 7

        Average Rating 5.0

        5

        7% enrolled

        It's super helpful. The good practice examples were a big help ^^

        • webmaster1570님의 프로필 이미지
          webmaster1570

          Reviews 2

          Average Rating 5.0

          5

          38% enrolled

          This is a great lecture that can be applied directly to practice. If the instructor has time, I hope that a lecture on data such as voice and text based on RNN will be opened.

          • sdf80367845님의 프로필 이미지
            sdf80367845

            Reviews 9

            Average Rating 4.8

            5

            72% enrolled

            It's the best. I feel lucky to be able to take this course at this price. I highly recommend it.

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