14 days with Claude Code
sorryhyun96
My 14-day use, starting right after release: Claude Code! (AI Coding) - Immediately applicable to job seekers and pros! I'll tell you right away why I recommend it to everyone.
입문
React, Python, Java
We help you accurately intuit the concepts of the theories that form the basis of deep learning.
Probability Theory, Mathematical Statistics, and Statistical Testing for Deep Learning
Essential Linear Algebra, Mathematical Notation
Other optimization theory, non-parametric methods, etc.
This course covers the fundamentals that remain relevant even amidst the rapid changes in deep learning. Even those new to deep learning theory will find it intuitive and easy to understand!
Understanding the underlying theory of deep learning means understanding the concepts necessary to understand deep learning, and can be quickly grasped through abstract yet intuitive explanations.
Below is a screenshot of the final part of Lecture 7, "Conditional or Independence." This course covers essential yet abstract and difficult concepts in deep learning, such as IID , in just 12 minutes + (Probability Chapter) Lecture 1 .
The course focuses on theory and does not include practical training. Because the coding environment is rapidly changing, with cloud-based chatGPT Agent coding environments like Dify, Langchain, and NPU-friendly OS, we aim to provide knowledge that will not be outdated by the emergence of new technologies .
Classical machine learning techniques that are still considered the fundamental principles of deep learning.
Uncertainty inference using probability theory
An optimization method that combines theoretical approaches from mathematics and heuristics from engineering.
The curriculum was designed around the three contents above.
Concept of statistics-based, linear models
This course covers basic machine learning techniques using statistical techniques and optimization theory.
We deliver only the most important content in linear algebra, optimization, and causal inference in an intuitive manner.
We'll also teach you how to read essential formulas and expressions, helping you progress to more advanced levels.
Probabilistic inference
This lecture will discuss how mathematical statistics and information theory have contributed to machine learning.
We will convey the content as intuitively as possible, including topics that were previously only mentioned in graduate school curricula, such as basic probability theory, mathematical statistics, Bayesian statistics, information theory, and lower risk bound.
Non-linear approaches
This course will cover how complex theories from the 2000s, such as the manifold hypothesis, kernel trick, multi-dimensional probability distribution, and non-parametric methods, relate to deep learning.
We will convey concepts close to the actual operation of deep learning, such as non-linear transformations of space.
We have been involved in various seminars aimed at conveying intuitive and accurate concepts, with the goal of sharing knowledge through activities such as fake research institutes.
He has diverse research and practical experience, including serving as a SIGUL 2024 workshop program committee member, ACL 2023 emergency reviewer, EMNLP 2023 invited reviewer, and publishing history in the Journal of the Korean Information Science Society.
For more detailed information, please refer to the notion resume .
Who is this course right for?
People who want to understand Deep Learning a little less abstractly
Someone recently growing skeptical of ChatGPT, LLMs
Those wishing to apply for AI Graduate School
Need to know before starting?
TOEIC 700 or higher English proficiency
High school humanities level math knowledge
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Thank you for your valuable lecture. Take care of your health.
They say that Corona is spreading again ㅜㅜ Smurf, stay healthy too~
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