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 💬
Q. Can you explain the formula?
I will explain the concepts rather than detailed formulas.
Q. Is it difficult to implement the code?
Let's implement the concept in the simplest way possible.
Q. Do you have any player knowledge?
You need a basic understanding of how to use TensorFlow and the normal distribution.
Guide to practical training environment and learning materials
Written in Google Colab.
Basic knowledge of Tensorflow is required.
Source code is provided as learning material.
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.
Recommended for these people
Who is this course right for?
Anyone who wants to understand and implement image-related deep learning algorithms
Anyone who needs a general understanding of image deep learning
