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Jee-Hyong Lee
This course covers fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing areas.
In the CNN part, you will learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. In the RNN part, you will learn the concept and the structure of RNNs, and the two variants of RNNs, LSTMs and GRUs.
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This course covers fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing areas.
In the CNN part, you will learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. In the RNN part, you will learn the concept and the structure of RNNs, and the two variants of RNNs, LSTMs and GRUs.
Read more
This course covers fundamental concepts of convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which are widely used in computer vision and natural language processing areas.
In the CNN part, you will learn the concepts of CNNs, the two major operators (convolution and pooling), and the structure of CNNs. In the RNN part, you will learn the concept and the structure of RNNs, and the two variants of RNNs, LSTMs and GRUs.
The goal of this course is to give learners basic understanding of CNNs and RNNs. Throughout this course, you will be equipped with skills required for computer vision and natural language processing.
Register for this course and see more details by visiting:
OpenCourser.com/course/0vwqz0/fundamentals
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What's inside
Syllabus
Week 1. CNN Basics
Week 2. Convolution and Pooling
Week 3. Structure of CNNs
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Syllabus
Week 1. CNN Basics
Week 2. Convolution and Pooling
Week 3. Structure of CNNs
Read more
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what should give you pause and
possible dealbreakers
Delves into deep learning models, which is a must-learn subject for those seeking a career in AI and machine learning
Taught by an expert in the field of natural language processing and deep learning
Examines how to address complex problems using deep learning models
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Reviews summary
Cnn 및 rnn의 핵심 기초
학습자들은 이 강좌가 CNN과 RNN의 기본 개념을 이해하는 데 매우 효과적이라고 말합니다. 특히 강사의 명확하고 체계적인 설명 덕분에 복잡한 내용도 쉽게 접근할 수 있었다는 평이 많습니다. 딥러닝 초보자에게 최적화되어 견고한 기초를 다지는 데 이상적이지만, 일부 학습자들은 심화된 내용이나 실제 코드 구현, 최신 트렌드가 부족하여 실무 적용에는 한계가 있다고 언급합니다. 전반적으로 깔끔한 자료와 유용한 실습 예제는 긍정적이지만, 수학적 배경이 부족하면 다소 어려울 수 있다는 의견도 있습니다.
딥러닝 입문자에게 CNN과 RNN의 탄탄한 기반을 제공합니다.
"이 강의는 CNN과 RNN의 기본 개념을 이해하는 데 정말 훌륭했습니다. ... 딥러닝 초보자에게 강력 추천합니다."
"이 코스는 CNN과 RNN에 대한 견고한 기초를 제공합니다."
"초보자에게 최적화된 강의입니다. 딥러닝에 대한 사전 지식이 거의 없었는데도, 이 강의를 통해 기본적인 컨볼루션 신경망과 순환 신경망의 원리를 이해할 수 있었습니다."
"딥러닝 기본기를 다지는 데 아주 좋은 강의입니다."
강사님의 설명 방식이 복잡한 개념을 이해하기 쉽게 만듭니다.
"강사님의 설명이 너무 명확해서 복잡한 내용도 쉽게 다가왔어요."
"명쾌한 설명 덕분에 CNN과 RNN의 원리를 확실히 이해했습니다. 교수님의 열정적인 강의가 인상 깊었고, 개념 설명이 아주 체계적입니다."
"강의 속도도 적절하고 핵심을 잘 짚어줍니다."
"교수님의 명확하고 체계적인 설명 덕분에 컨볼루션 신경망과 순환 신경망의 원리를 쉽게 파악할 수 있었습니다."
일부 학습자는 수학적 배경 부족으로 어려움을 겪을 수 있습니다.
"저는 초보자인데도 불구하고 좀 어렵게 느껴졌습니다. 이론 설명은 좋았지만, 수학적 배경이 충분치 않으면 따라가기 힘들 수도 있습니다."
"선수 지식에 대한 안내가 더 명확했으면 좋겠습니다."
이론 중심이며, 실무 적용을 위한 심화 코드 예제가 부족합니다.
"몇몇 부분에서는 좀 더 심화된 내용이나 코드 구현에 대한 부분이 부족하게 느껴졌습니다. 이론은 탄탄하나, 실제 응용에 대한 부분이 살짝 아쉬웠습니다."
"강의 내용은 나쁘지 않지만, 최신 트렌드나 고급 기법에 대한 언급이 거의 없습니다. ... 자료가 살짝 오래된 느낌도 있습니다."
"이론적인 부분은 괜찮지만, 실제 코딩 예제나 응용 학습이 부족하다고 느꼈습니다. 실무에 바로 적용하기에는 한계가 있어 보입니다."
"더 깊이 있는 내용을 원한다면 다른 강의를 찾아야 할 수도 있습니다."
Activities
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Career center
Learners who complete Fundamentals of CNNs and RNNs will develop knowledge and skills
that may be useful to these careers:
AI Engineer
AI Engineer
An AI Engineer may wish to study the concepts of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) to improve their ability to create models for computer vision and natural language processing. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University covers the fundamental concepts of CNNs and RNNs. This course can help AI Engineers build a foundation that will enhance their work.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use CNNs and RNNs to train models for various tasks in computer vision, natural language processing, and other domains. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University provides a solid foundation in these concepts, enabling Machine Learning Engineers to build and deploy more effective models. This course is especially beneficial for those seeking to specialize in computer vision or natural language processing.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers utilize CNNs and RNNs to develop deep learning models for various applications. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University provides a comprehensive overview of these techniques, empowering Deep Learning Engineers to design and implement more sophisticated deep learning models. This course is a valuable asset for those seeking to advance their skills in deep learning.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers leverage CNNs to develop computer vision systems for various applications, such as image recognition, object detection, and medical imaging. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University provides a strong foundation in CNNs, empowering Computer Vision Engineers to build and deploy more accurate and robust computer vision systems.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers employ RNNs to develop natural language processing systems for various applications, such as machine translation, text summarization, and sentiment analysis. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University provides a comprehensive overview of RNNs, enabling Natural Language Processing Engineers to build and deploy more effective natural language processing systems.
Data Scientist
Data Scientist
Data Scientists may utilize CNNs and RNNs for various tasks in data analysis and machine learning, such as image classification, natural language processing, and time series forecasting. The Fundamentals of CNNs and RNNs course from Sungkyunkwan University provides a foundational understanding of these techniques, enabling Data Scientists to expand their skillset and tackle more complex data science problems.
Software Engineer
Software Engineer
Software Engineers working in computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University beneficial. This course provides a solid foundation in these concepts, enabling Software Engineers to develop more sophisticated and effective software solutions in these domains.
Research Scientist
Research Scientist
Research Scientists working in artificial intelligence, computer vision, or natural language processing may benefit from the Fundamentals of CNNs and RNNs course from Sungkyunkwan University. This course provides a comprehensive overview of these techniques, enabling Research Scientists to conduct more advanced research and develop innovative solutions in these fields.
Computer Science Professor
Computer Science Professor
Computer Science Professors teaching courses in computer vision, natural language processing, or deep learning may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University useful. This course provides a comprehensive overview of these topics, enabling Computer Science Professors to stay up-to-date with the latest advancements and effectively teach these concepts to their students.
Data Analyst
Data Analyst
Data Analysts working with image data or text data may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University helpful. This course provides a foundational understanding of these techniques, enabling Data Analysts to extract more insights from their data and develop more accurate and reliable data analysis models.
Business Analyst
Business Analyst
Business Analysts working in industries that utilize computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University beneficial. This course provides a basic understanding of these technologies, enabling Business Analysts to better understand the potential applications and limitations of these technologies in their business context.
Product Manager
Product Manager
Product Managers working on products that incorporate computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University helpful. This course provides a basic understanding of these technologies, enabling Product Managers to make more informed decisions about the features and capabilities of their products.
Marketing Manager
Marketing Manager
Marketing Managers working on campaigns that utilize computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University helpful. This course provides a basic understanding of these technologies, enabling Marketing Managers to better understand the capabilities and limitations of these technologies and develop more effective marketing strategies.
Sales Manager
Sales Manager
Sales Managers working with clients in industries that utilize computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University helpful. This course provides a basic understanding of these technologies, enabling Sales Managers to better understand the needs of their clients and effectively communicate the benefits of their products or services.
Financial Analyst
Financial Analyst
Financial Analysts working with companies that utilize computer vision or natural language processing may find the Fundamentals of CNNs and RNNs course from Sungkyunkwan University helpful. This course provides a basic understanding of these technologies, enabling Financial Analysts to better understand the potential risks and opportunities associated with these technologies and make more informed investment decisions.
For more career information including salaries, visit:
OpenCourser.com/course/0vwqz0/fundamentals
Featured in The Course Notes
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Fundamentals of CNNs and RNNs:
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Reading list
We've selected seven books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Fundamentals of CNNs and RNNs.
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Provides a comprehensive overview of deep learning, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of recurrent neural networks, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about RNNs.
Provides a comprehensive overview of natural language processing with deep learning, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about NLP.
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Provides a comprehensive overview of deep learning for computer vision, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of deep learning with Keras, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about deep learning.
Provides a comprehensive overview of natural language processing with spaCy, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about NLP.
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Provides a comprehensive overview of computer vision with OpenCV, covering the latest advances in the field. It valuable resource for anyone who wants to learn more about computer vision.
For more information about how these books relate to this course, visit:
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Effort
4 hours/week
Level
Introductory
Via
Coursera
Institution
Sungkyunkwan University
Instructor
Jee-Hyong Lee
Language
한국어
Featured in The Course Notes
Read
one article that features
this course,
Fundamentals of CNNs and RNNs
from our blog, The Course Notes:
To read more articles from OpenCourser, visit:
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