Image generation model that is implemented and understood - Utilization

Image deep learning concepts
Understand perfectly 🧑‍💻

Understand and implement image-related deep learning algorithms. This lecture covers the implementation of pre-trained models, class activation maps, Yolo, PIX2PIX, and CYCLE GAN.

I've been studying deep learning for over five years, even though I'm not a major in the field. Image generation models are incredibly fascinating, and I'm always studying them. However, every resource I find presents such complex code implementations that I find analyzing the code more challenging than studying the actual model itself. To help you avoid the same struggles I did, I'll focus on the core concepts and implementations, along with a simple, easy-to-follow guide.

• We will walk you through the entire coding process together.
• Rather than using detailed formulas, I will explain with an easy-to-understand example.
• Let's run it in Colab.
• You should be able to handle Tensorflow simply.

To these people
I recommend it.

_{Dealing with images
About deep learning
Anyone curious}

_{Overall image deep learning
Direction
Those who want to study}

_{Through direct implementation
Image deep learning
Anyone who wants to understand}

What you'll learn 📚

There's plenty of reference code online, but aren't you finding it difficult to implement it yourself? Let's solidify our understanding of image deep learning concepts with a simple implementation.

Class Activation Map

Let's visualize what the deep learning model focuses on.

YOLO

Let's find objects in an image, identify them, and mark their location information.

Style Transfer

Let's apply a different style to the image.

Pix2Pix

Let's train a GAN using pair images.

Cycle Gan

Let's implement Image 2 Image Translation using unpaired image data.

Let's implement stable diffusion using diffusion.

Q&A before class 💬

Introducing the Knowledge Sharer ✒️

Hello! My name is [Awesome]. I'm a non-major and have been studying deep learning for over five years. Deep learning is so much fun. I'm particularly fascinated by image generation models. While researching, I often found the concepts or code to be too complicated. I'd like to share what I've learned. As a non-major, I've often questioned whether it's appropriate to share this lecture. However, I believe there are definitely people in a similar situation, so I've gathered my courage and decided to share it.