The First Step into AI Without Math: From Basics to the Latest Trends
This lecture is designed to give you a general understanding of AI without overwhelming you with difficult topics like mathematical formulas. It's perfect for learning with a light and easy mindset.
The distinctions between AI, Machine Learning, and Deep Learning, and the stories behind them.
Why is collecting high-quality data important?
What is the process for AI services and AI research?
Deep learning learns from data by:
How are image processing and natural language processing performed?
How do the currently popular chatbots and Diffusion models understand user intent and generate images that don't exist in the world?
What on earth is artificial intelligence? I'll answer your questions really easily.
I'm curious about what artificial intelligence is. Do I really need to know difficult math? 🙄
AI is penetrating so deeply into our daily lives. From Siri and Bixby on our phones to YouTube recommendation algorithms... we are living with AI without knowing it.
So, I became curious about AI, machine learning, and deep learning, and I googled them one by one. But... as soon as the content gets a little difficult, a lot of mathematical formulas and unknown pictures start to appear.
I was just wondering how AI learns images ...
I was just wondering how Tesla drives autonomously! (Source: Tesla Official YouTube)
I was just wondering how AI creates images!
Stop math...!
Leave out the difficult parts Artificial intelligence that is really easy to learn
What is the difference between AI, machine learning, and deep learning ? They say AI learns... but how does it learn?
How does a computer interpret images and Korean? You're saying you're going to learn on your own ...?
YouTube and Instagram are the places where I can find content I like. How do you classify it...?
They say that data is needed to train AI. What on earth is this data we're talking about here...?
Artificial intelligence was difficult to study because of fragmented concepts and difficult academic papers! A PO with a background in research in the AI industry will explain artificial intelligence and deep learning in an easy and detailed manner using various examples while removing as much difficult content as possible.
✅ You can solve your worries through this lecture.
Let's assume that you are creating a virtual AI speaker and learn about the AI service life cycle.
You can also learn various concepts for evaluating deep learning.
You will learn step by step how AI development and research proceeds and what to consider.
And through simple examples, we learn how deep learning is done and how models are trained.
How to do image processing,
Learn how to do natural language processing. And of course, you'll get hands-on practice!
And we also carry out projects using the latest models.
Learn about these things 📖
The overall concept and characteristics of AI
AI service flow and AI research process
Basic learning methods of deep learning
Image features and image processing deep learning models
Natural language features and natural language processing deep learning models
The flow of deep learning model development learned by creating a simple deep learning model
(Project) Experience the model that forms the basis of a chatbot using KLUE data
(Project) Experience the cutting edge of generative models using the Diffusion model
Even if you don't know math or coding Anyone can understand AI.
I recommend this to these people
🙋♀️ Know about AI and deep learning I want to do a difficult math formula People who gave up because of that
🙋♂️ AI in products and services I want to apply it, but to AI People who have no knowledge of Korea
💁♀️ In the plan and proposal AI related content that comes out For those who want to understand
💁♂️ For those who wish to enter the AI field Data Science Non-major
Specially prepared
✅ Easy theory explanation that anyone can understand
✅ Lecture materials PDF file
✅ Practice Code
✅ Practice Materials
In this lecture Knowledge sharer is
Kim Ji-hoon
Hello. My name is Jihoon Kim, and I am currently working as a Product Owner at a startup that generates data for AI. I am very interested in AI, data, and education, and I am constantly trying and thinking about how to explain it easily to others.
Related history
(Current) Select Star Product Owner
(Former) Tmax Group AI Researcher/Team Leader
(Educational activities) Naver Connect Foundation mentor, KT AICE instructor, university special lecturer
💡 AI may seem vaguely difficult, but we'll help you get started!
What kind of logic does AI service work on? Why is collecting high-quality data important? What is the difference between natural language processing and image processing? ... We often hear that artificial intelligence is important, but it is difficult to find explanations for things we are curious about. Even if you search the Internet, the content is scattered and the complicated explanations make it difficult to understand.
The lecture, “First Steps in Artificial Intelligence without Math,” is a lecture that gathers fragmented concepts about artificial intelligence and deep learning as a kind of introduction. While excluding complex mathematics as much as possible, it helps you build prior knowledge through easy-to-understand explanations and light-challenging deep learning exercises. Were you curious about AI but didn’t know where to start and how to learn it? I hope this lecture can be a good start for you. If you are interested in the lecture, be sure to watch the [Lecture Overview] video that was released as a preview! 🙂
Recommended for these people
Who is this course right for?
For those who are planning an AI service but feel lost and don't know where to start.
People who wanted to study AI but gave up because of math.
For those curious about how AI actually learns from data.
For those who want someone to summarize AI all at once.
It's very helpful because it's explained in a way that even non-majors can understand! The content and materials are very clean. I'm now able to see a better picture of how to apply artificial intelligence, which I was only vaguely interested in, to my work!