First-Time Deep Learning and PyTorch Bootcamp (Easy! From the Basics to the Core Transformers Behind ChatGPT) [Data Analysis/Science Part 3]

Hello. This is Dave Lee from Fun Coding.

The pace of AI development these days is truly astonishing, isn’t it?

It seems that, along with IT skills, the ability to use AI has also become important.

So, I’m sharing a related video to help you casually keep up with the latest AI alongside the course.

The video I’m sharing this time is about the newly updated AI Jev 1.14.

With this update, it can now view and make judgments based not only on text but also on images.

I directly compared images and text using the same 50 questions as OpenAI’s GPT-6 Luna and summarized the results.

Direct Comparison of the New Jev 1.14 and GPT-6 Luna with 50 Questions

https://youtu.be/RJJOVHZxyhc

I hope this was helpful.

Thank you.

Hello. This is Dave Lee from Fun Coding.

The pace of AI development these days is truly amazing.

Along with IT skills, the ability to use AI also seems to be becoming increasingly important.

So, I’m sharing relevant videos from time to time to help you learn about the latest AI alongside the course.

The video I’m sharing this time is about Laya, a free, open-source AI.

Laya is the same type of AI as Jev, and because it’s open source, you can run it for free on your own computer.

I’ve compiled five use cases, installation instructions, and the results of comparing Laya with Jev in terms of speed and accuracy using the same questions.

Can Free, Open-Source Laya Replace Jev?

https://youtu.be/kzSpE6SCrV8

I hope this is helpful.

Thank you.

Hello, this is Dave Lee from Fun Coding.

AI’s pace of development these days is truly astonishing.

Along with IT skills, the ability to use AI also seems to have become important.

So, along with the courses, I’d like to occasionally share relevant videos to help you keep up with the latest AI in an easygoing way.

The video I’m sharing this time is about Jev, a newly released AI.

Jev is a somewhat new type of AI that doesn’t write text, but only assigns probabilities to the available options.

I’ve compiled seven use cases and how to try it out yourself.

7 Use Cases and How to Use the New AI Jev

https://youtu.be/nU0dsSJkPZw

I hope this was helpful.

Thank you.

Hello.
I hope you've been well.

I wanted to share, rather casually, that my book has been published.
I published the revised edition of 『The Complete Guide to Claude Code and Codex CLI』 with Gilbut.

Whatever you're doing, the best results come from using both tools together rather than relying solely on Claude Code or Codex CLI.
That said, both tools have quite a lot of features. You don't need to know every single one, but I thought having a reference book would be helpful. In addition, I have organized the essentials, covering everything from how to use both tools together in a single project for vibe coding to advanced engineering techniques such as harnesses and loops.

https://www.yes24.com/product/goods/197193249

I’m still thinking about which AI course to open on Inflearn.
Based on the extensive feedback we’ve received, we’ll prepare a great AI course and bring it to you.
Thank you.

Hello. This is Dave Lee from Fun Coding.

I need to translate this Korean text to English while preserving any markers and following the guidelines. The text is: "다름이 아니라 딥러닝 강의에서 수정 사항이 있어서 가볍게 공유를 드립니다." This is a polite, formal Korean sentence. Let me break it down: - "다름이 아니라" - a polite opening phrase meaning "it's nothing special but" or "I'm reaching out because" - "딥러닝 강의에서" - "in the deep learning course/lecture" - "수정 사항이 있어서" - "there are some corrections/modifications" - "가볍게 공유를 드립니다" - "I'm sharing (this) briefly/lightly" (polite form) Translation: I

There was an error in the test_loss calculation part during test evaluation in the "Applying CNN to MNIST Classification Problem" chapter. We apologize for the inconvenience. Therefore, we have updated the related materials with the following context.

nn.NLLLoss() has reduction='mean' applied by default, so it returns the average loss of samples within each mini-batch. Therefore, the test_loss accumulated through the for loop becomes "the sum of mini-batch average values".

In the existing code, this value was divided by the total number of samples (len(test_batches.dataset), e.g., 10000), which results in a much smaller value than the actual loss. For correct calculation, it should be divided by the number of mini-batches (len(test_batches), e.g., 79).

In other words, since it's the sum of mini-batch averages, dividing by the number of mini-batches accurately calculates the overall average loss.

I understand you want me to translate "수정된 파일:" from Korean to English. Modified files:

• I understand you want me to translate the content of "12_CNN_MNIST.ipynb". However, I don't see any Korean text content provided to translate.

• # Translation Analysis This appears to be a filename for a Jupyter notebook file. According to the translation guidelines: 1. Filenames should generally be preserved as they are technical elements 2. The components are

• # Translation Analysis This appears to be a filename for a Jupyter notebook. According to the technical requirements, I should preserve filenames, code elements, and technical terms unchanged. # Translation 12_CNN_MNIST_GPU_DROPOUT.ipynb

I have completed the modifications to the Jupyter Notebook materials, so if you download the course materials again, you can study with the updated code.

Thank you.

Hello. This is Dave Lee from Janjaemi Coding.

The reason is that we have opened a new lecture and are sharing it with you briefly.

• Computer Science (CS Knowledge) Bootcamp for Beginners | Computer Architecture, Operating Systems, Networks (CS Knowledge Part 1)

This lecture systematically organizes the core of computer engineering (CS knowledge) that developers or data specialists preparing for careers or working in the field must know. Among the various subjects of computer engineering (CS knowledge) , three subjects were organized into one lecture so that you can efficiently learn the important computer structure, operating system, and network . In addition, to prepare for technical interviews, the core technical interview questions for each subject, special answer methods, and model answers were included.

Computer science knowledge is essential foundation knowledge in the development and data fields, but its importance is often overlooked. I also emphasize to my acquaintances and family that if you are preparing to become a developer or data specialist, you should definitely learn this theory, which is absolutely necessary in the mid- to long-term .

However, many people have difficulty organizing it into individual subjects because of the large volume. To solve this difficulty , the content has been neatly organized in the form of online lectures . While covering key concepts in depth, various explanation methods have been used to help with clear understanding .

This is a proven lecture that has been selected by tens of thousands of students and used as a training material for new employees by leading domestic IT companies, after being reworked several times over the past five years. This time, it has been reworked and improved and opened again. I hope it will be helpful.

We've also included a new opening discount.

thank you

Remnant fun coding dream

Hello. This is Dave Lee from Janjaemi Coding.

I am sharing the lectures lightly by renewing them. It is a continuation of the lectures that were renewed just before.

• [Renewal] Python Data Analysis for Beginners (Easy! Learn Preprocessing, Pandas, and Visualization) [Data Analysis/Science Part 1]

• [Renewal] Python Machine Learning Bootcamp for Beginners (Easy! Organize by Solving Real Kaggle Problems) [Data Analysis/Science Part 2]

The following lectures have been renewed.

• [Renewal] Introduction to Python Backend and Web Technology for Beginners (Intermediate Python, Basics of Backend and Web Technology with Flask) [Full Stack Part 1-1]

The existing Python Backend and Web Technology Bootcamp course titles have also been changed to match the purpose of the course, which is to organize the core web technology concepts, intermediate Python grammar, and backend basics that must be understood for full-scale development . The course content has been improved to reflect the latest trends and the fastest full-stack development architecture .

Through this course, you can effectively learn Python intermediate grammar (including intermediate object-oriented) essential for practical development, web/app core concepts, and the latest full-stack architecture. Those who have already taken the course can listen to it again. Updated videos are marked with (updated) in the chapter name, so that those who have already learned can check only the updated parts. We will continue to provide a satisfactory learning experience through continuous updates.

Just like this lecture, I will continue to improve other lectures in the future.

I hope the related lectures will be helpful to you.

thank you

Hello. This is Dave Lee from Janjaemi Coding.

I've renewed the lecture and am sharing it with you in a lighter way.

Following the lecture that was just renewed
[Renewal] Python Data Analysis for Beginners (Easy! Learn Preprocessing, Pandas, and Visualization) [Data Analysis/Science Part 1]

The following lectures have been renewed.

[Renewal] Python Machine Learning Bootcamp for Beginners (Easy! Solve actual Kaggle problems and organize them) [Data Analysis/Science Part 2]

We have improved the parts that seemed lacking in the existing lectures and improved them to match the latest libraries. We have also improved the lectures by adding stories from a more practical perspective. Those who have already taken the lectures can listen to them again.

Just like this lecture, I will continue to improve other lectures in the future.

I hope the related lectures will be helpful to you.

thank you