
핵심만 빠르게, 입문자를 위한 파이썬(Python)과 판다스(Pandas)
Jiwoon Jeong
파이썬(Python)과 판다스(Pandas) 라이브러리의 핵심적인 개념을 빠르게 익힐 수 있는 강의입니다. 각 파트 뒤에 있는 연습 문제를 함께 해결해 나가며 프로그래밍 역량을 향상시켜보세요!
입문
Python, Pandas
This is a lecture where you can learn Deep Learning using TensorFlow. Learn linear regression, logistic regression, and softmax regression models, and finally create an MLP model that can classify fashion images!

Fashion image data classification using deep learning
Multi Layer Perceptron
Softmax Regression
Logistic Regression
Linear Regression
Creating a deep learning model using TensorFlow
It is true that studying deep learning is difficult. Not only does it require a variety of mathematical and conceptual understanding, but it also requires programming skills to actually create a model.
The purpose of this course is to provide beginners with the basic knowledge necessary to understand and utilize Deep Learning and Tensorflow . After taking this course, you will be able to implement simple deep learning models and understand deep learning frameworks such as Tensorflow.
One of the biggest concerns beginners have is that deep learning seems vaguely too difficult. Terms and concepts such as Optimizer, Loss function, and SGD seem very difficult. To solve this, this lecture clearly explains the core concepts so that you can acquire basic knowledge of deep learning and TensorFlow, and it is structured so that you can learn each concept through practice and examples.
Another concern that beginners often have is how to actually use deep learning frameworks such as Tensorflow. The lecture explains the process of actually creating a model and training it with specific examples, so it helps beginners understand deep learning by directly using Tensorflow!
Build your programming foundation
For those who want to learn deep learning
Deep learning, Tensorflow as the core
For those who want to learn quickly
Increased understanding of deep learning and TensorFlow
The course covers everything from the basic concepts of deep learning and TensorFlow to practical training. By experiencing the process of actually implementing and utilizing models, you can greatly increase your understanding of deep learning and TensorFlow!
Increasing confidence in the field of deep learning
Through this lecture, you will gain basic knowledge and implementation skills about deep learning and TensorFlow, which will increase your confidence in the field of deep learning. This confidence will motivate you to study deep learning in the future!
To avoid being too caught up in theory and not missing out on practical training, we've included key content for those who are new to deep learning.
We will study the basic models that are the foundation for creating full-fledged deep learning models, from Linear Regression to Logistic Regression and Softmax Regression.
To help you learn more effectively, we've divided it into concept and practice sections. In addition, we've made it so that anyone can easily practice regardless of the environment by utilizing Google Colab.
Q. How much programming and math knowledge do I need?
First of all, regarding programming, I assume you can do basic Python programming. Of course, I have also created a Python basics course, so you can take it first, right?! 😊😊 As for math, you only need to know up to differentiation. If you forgot, let's briefly review the math you learned in high school!
Q. How is the internship conducted?
Google Colab provides a Jupyter development environment in a cloud environment, so anyone can easily follow along regardless of their computer specifications.
Who is this course right for?
College students/office workers interested in deep learning
People who want to learn the basics of deep learning
Need to know before starting?
Concept of differentiation
Python Basics
6,990
Learners
198
Reviews
170
Answers
4.9
Rating
4
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딥러닝 기초 처음 접하는 사람한테 진짜 엄청나게 추천하는 강의 입니다 꼭 한번 들어보세요
도움이 되셨다니 기쁩니다. ㅎㅎ 좋은 평가 감사합니다!!
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