Learning OpenAI Codex through Projects - From Basics to Advanced Vibe Coding Using AI
AISchool
Non-Majors Welcome: Real-World Vibe Coding Projects Created Through Conversations with AI
Beginner
Business Productivity, openai, codex
This course teaches you how to implement deep learning papers by implementing the YOLO (You Only Look Once) paper from scratch using TensorFlow 2.0.
509 learners
Level Intermediate
Course period Unlimited
Reviews from Early Learners
5.0
Daniel Park
From the perspective of using machine learning and deep learning in the field, this lecture broadened my frame of mind so that I could expand my career from being a 'developer' who uses existing well-structured models, to a 'researcher'. I was able to follow along well without missing the details of the mathematical part, and I was also able to understand the process of integrating this into actual implementation code. I hope that you will launch a lecture that goes beyond this lecture and covers representative papers such as BERT or GPT, or widely known techniques in model development.
5.0
김홍직
thank you
5.0
김정윤
Good good good good good
How to read deep learning papers
How to implement deep learning papers
A detailed understanding of the YOLO model architecture
Background knowledge on the Object Detection problem domain
How to write code using TensorFlow 2.0
An essential skill for deep learning researchers: the ability to implement the latest research papers!
Learn with YOLO implementation 😀
Many companies, when hiring deep learning researchers, prioritize experience implementing cutting-edge research papers . Gain hands-on experience implementing the YOLO (You Only Look Once) paper yourself.
After reading the YOLO paper together and fully understanding the YOLO structure✍️,
Let's implement YOLO ourselves using TensorFlow 2.0.👨🏻💻
We'll read the YOLO paper (You Only Look Once: Unified, Real-Time Object Detection) and implement the YOLO model from scratch using TensorFlow 2.0 . We'll also create a cat detector using the implemented YOLO model.
👋 This course requires prior knowledge of TensorFlow 2.0 and the fundamentals of deep learning. Please take the following courses first, or obtain equivalent knowledge before taking this course .
This course teaches you the core theories of deep learning and how to implement deep learning code using the latest TensorFlow 2.0.
Q. What are the benefits of experiencing implementing deep learning papers?
Who is this course right for?
Those who want to develop the ability to read and implement deep learning papers
Those who want to get a job related to deep learning research
Anyone who wants to conduct research related to artificial intelligence/deep learning
Those preparing for graduate school in artificial intelligence (AI)
Need to know before starting?
Experience using Python
Experience of attending the pre-course [Introduction to Deep Learning with TensorFlow 2.0]
10,173
Learners
840
Reviews
362
Answers
4.6
Rating
32
Courses
All
30 lectures ∙ (3hr 37min)
Course Materials:
All
38 reviews
4.7
38 reviews
Reviews 1
∙
Average Rating 5.0
5
Good good good good good
Hello. Thank you for taking the time to take the class~!. I will try my best to create more satisfactory lectures. Have a nice day!
Reviews 1
∙
Average Rating 5.0
5
It was a good lecture
Hello. Thank you for taking the time to take the class~!. I will try my best to create more satisfactory lectures. Have a nice day!
Reviews 2
∙
Average Rating 5.0
5
From the perspective of using machine learning and deep learning in the field, this lecture broadened my frame of mind so that I could expand my career from being a 'developer' who uses existing well-structured models, to a 'researcher'. I was able to follow along well without missing the details of the mathematical part, and I was also able to understand the process of integrating this into actual implementation code. I hope that you will launch a lecture that goes beyond this lecture and covers representative papers such as BERT or GPT, or widely known techniques in model development.
Thank you~. We plan to open various lectures in the future, so please look forward to it~. Have a nice day!
Reviews 1
∙
Average Rating 5.0
5
thank you
Hello. Thank you for taking the time to take the class~!. I will try my best to create more satisfactory lectures. Have a nice day!
Reviews 1
∙
Average Rating 3.0
3
This is not a useful lecture for beginners. Here, beginners mean people who have not written deep learning code with expert type code. At least, it would be useful if you have written deep learning code in a functional way and can partially use other people's deep learning code through git cloning. However, it would be useful if you have not previously graduated from a related department and are thinking of writing code yourself in graduate school or want to experience it, and if you are willing to look over the entire course and make additional supplements according to your career development. The quality of the lecture itself is not that high. Most of the explanations and lecture materials are not very friendly or detailed, and the handwriting is done with a mouse rather than a Wacom pen. However, since there is no other lecture that can replace it, it has the advantage of being a lecture that implements papers into code.
Hello. Thank you for taking the time to take the class~!. Thank you for the detailed course review~. I will try my best to create a more satisfactory course. Have a nice day!
Check out other courses by the instructor!
Explore other courses in the same field!
Limited time deal ends in 7 days
$1,461,624.00
29%
$77.00