[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 and practical examples that can be used immediately in the field.
This course is prepared for Intermediate Learners.
What you will learn!
Understanding Deep Learning-Based Object Detection and Segmentation
Deep theoretical learning on RCNN series, SSD, YOLO, RetinaNet, EfficientDet, 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 a variety of difficult practical examples to reach a level where you can directly apply Object Detection/Segmentation in practice.
Acquire various basic knowledge that constitutes Object Detection/Segmentation
Train on custom datasets and create your own models using various implementation packages
Learn the pros and cons of various Object Detection/Segmentation models through hands-on examples.
Handle major data sets such as Pascal VOC, MS-COCO, etc. and convert TFRecord
Apply annotations to the dataset using the CVAT Tool and create your own training data
Lower the hurdles, go deeper at your core!
Become a Deep Learning CNN expert.
Meet the latest revised edition
Deep Learning Computer Vision Training.
Average rating 4.9★ Chosen by 1,300+ students,
Infraon Bestseller Complete Renewal in 2021!
Hello, this is Kwon Chul-min.
Thanks to the support of many people, we have now released a revised edition of 'Deep Learning Computer Vision Complete Guide' .
About 90% of the videos in the existing lectures have been newly created, and we will introduce improved and additional content.
Based on the feedback you have sent to the lectures, we have created a revised edition focusing on the following points.
- A more detailed theoretical explanation of the topics that students have frequently asked questions about
- Hands-on training based on the latest/highest performing Object Detection/Segmentation package
- Reflecting the latest trends in Object Detection/Segmentation
- Writing more flexible, diverse, and scalable hands-on code + more detailed explanations
- Various other additional classes
The revised lecture
Lecture Introduction 📝
The center of deep learning computer vision technology is rapidly shifting to Object Detection and Segmentation .
▲Intelligent image information recognition ▲AI vision inspection smart factory ▲Automatic medical image diagnosis ▲Robotics ▲Autonomous vehicles, etc. Deep learning-based Object Detection and Segmentation technologies are spreading in many fields. Accordingly, leading domestic and international AI companies are also sparing no investment in this field and seeking to secure development personnel.
Two trends finally met, Object Detection & SegmentationIn recent years, the field of Object Detection and Segmentation has been developing rapidly, and the demand for talents with relevant practical skills is increasing. However, as it is a cutting-edge field that applies deep learning, there is a lack of books, materials, and lectures for learning, making it difficult to train appropriate personnel.
As a deep learning computer vision expert
We will guide you to be reborn.
This course consists of in-depth theoretical explanations of Object Detection and Segmentation and many practical examples that can be used immediately in the field , and will help you become a deep learning-based computer vision expert needed in the field.
Starting with an easy explanation of the concept
Even to in-depth theory.
We will clearly explain everything from easy concepts to in-depth theories about the vast field of Object Detection/Segmentation, including RCNN series, SSD, YOLO, RetinaNet, EfficientDet, and Mask RCCN.
You can thoroughly learn the concepts with detailed lecture notes.Through practical examples
Maximize your deep learning practical capabilities.
There is no better way to improve your practical skills than by coding and implementing things yourself.
This course consists of many practical examples that will help you maximize your practical implementation skills in Object Detection and Segmentation.
©SLAM DUNKTo these people
I recommend it.
How does deep learning CNN work?
Can it be applied in practice?
Anyone who was worried
Deep learning based
Computer Vision Solutions
For those who want to develop
Deep learning image classification capabilities
Up to date with the latest CV technology
Those who want to expand
Going to graduate school for artificial intelligence,
Deep Learning-Based CV Field
Job seekers/Job changers
Please check your player knowledge.
- Experience with Python programming and basic understanding of deep learning CNNs are required.
- Also, some experience with TF.Keras or Pytorch would be a plus.
Hard to see anywhere
The latest CV technologies all in one place.
Very good performance
State-of-the-art Object Detection/Segmentation implementation
Practice using packages
MMDetection, Ultralytics Yolo, AutoML EfficientDet, etc. 
Inference Practice Using General-Purpose OpenCV DNN and Tensorflow Hub
For various images and videos
Object Detection/Segmentation Practice
Various cases where computer vision technology is actually used
With multiple custom data sets
Model Training Practice
Various custom data setsAs a deep learning computer vision expert, you should be able to train models with multiple custom data sets to produce your own Object Detection/Segmentation model. You should also be able to improve and evaluate the performance of this model.
This course will teach you the ability to train custom data sets and create optimal inference models using various implementation packages.
With a self-created training data set
Custom Model Training / Inference Practice
Practice with your own training data setUsing the annotation tool CVAT, we will create a training dataset that applies bounding box annotations to general images, and practice Custom Model Training and Inference using the dataset created in this way.
Practice Environment 🧰
All practical code used in the lecture was written based on the Google Colab environment.
Google Colab, Kaggle logoWe will conduct hands-on training based on GPU, and if Colab's free GPU allocation is not sufficient, we also recommend using Colab Pro. (※ Colab Pro costs about $10 per month.)
If you do not have enough Colab GPU free kernel resources, you can use the Kaggle kernel. We also provide separate practice codes made for Kaggle. You can hear more detailed information about the practice environment by referring to the Section 0 - [Setting up the practice environment] class.
Please check before taking the class!
- If you do not use GPU kernels such as Colab or Kaggle, you may have difficulty following the examples. We ask for your understanding in advance.
Practice Code and Lecture Materials 👨💻
The practice code can be downloaded from https://github.com/chulminkw/DLCV_New . Reviewing the practice code in advance will help you get a sense of the level of programming required to understand the practice.
Provides 320 pages of lecture PDF textbookThe textbook used in the lecture (320 pages) can be downloaded from Lecture Section 0: Lecture Textbook .
To learn the theory
There is no better way than practice.
Don't wait until you fully understand the theory of deep learning. There is no better way to learn the theory than through practice.
Once we start coding, our brains follow to understand the material. Let's implement the various practical examples presented in the lecture with me. If you listen to the lecture and implement it yourself by pressing the keyboard, the parts that felt like clouds in the sky will gradually become real.
To become an expert, sometimes (I think most of the time) you have to run before you can learn to walk. This course will be your best companion to help you develop your career and capabilities in the field of deep learning-based computer vision.
thank you
― What Tony Stark said to Jarvis during the Iron Man suit test in <Iron Man 1>
“Sometimes you have to run before you can walk.”
Recommended for
these people!
Who is this course right for?
Anyone interested in deep learning
Those who have studied theory-based Object Detection and Segmentation based on deep learning
Anyone who has ever wondered how deep learning CNNs can be applied in practice
Those who want to expand their capabilities beyond deep learning CNN image classification to object detection/segmentation
Anyone who wants to develop deep learning-based solutions in the field of computer vision
Anyone who wants to challenge Object Detection/Segmentation Challenge in Competition such as Kaggle
For those preparing for AI graduate school
Those who are preparing to move to the field of Computer Vision based on deep learning
Need to know before starting?
Python programming experience
Basic understanding of deep learning CNN
(Optional) Minor experience with TF.Keras or Pytorch
Hello
This is 권 철민
Students
23,083
Reviews
1,060
Rating
4.9
Courses
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Curriculum
All
169 lectures ∙ (38hr 5min)
are provided.