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신경망 및 딥 러닝
Younes Bensouda Mourri
Younes Bensouda Mourri
딥 러닝 전문화의 첫 번째 과정에서는 신경망과 딥 러닝의 기본 개념을 학습합니다.
마지막에는 완전히 연결된 심층 신경망의 구축, 훈련 및 적용, 효율적인(벡터화된) 신경망 구현, 신경망 아키텍처의 주요 파라미터 식별, 딥 러닝을 자체 애플리케이션에 적용 등 딥 러닝의 부상을 주도하는 중요한 기술 동향에 익숙해질 것입니다.
Read more
딥 러닝 전문화의 첫 번째 과정에서는 신경망과 딥 러닝의 기본 개념을 학습합니다.
마지막에는 완전히 연결된 심층 신경망의 구축, 훈련 및 적용, 효율적인(벡터화된) 신경망 구현, 신경망 아키텍처의 주요 파라미터 식별, 딥 러닝을 자체 애플리케이션에 적용 등 딥 러닝의 부상을 주도하는 중요한 기술 동향에 익숙해질 것입니다.
Read more
딥 러닝 전문화의 첫 번째 과정에서는 신경망과 딥 러닝의 기본 개념을 학습합니다.
마지막에는 완전히 연결된 심층 신경망의 구축, 훈련 및 적용, 효율적인(벡터화된) 신경망 구현, 신경망 아키텍처의 주요 파라미터 식별, 딥 러닝을 자체 애플리케이션에 적용 등 딥 러닝의 부상을 주도하는 중요한 기술 동향에 익숙해질 것입니다.
딥 러닝 전문화는 딥 러닝의 기능, 과제 및 결과를 이해하고 첨단 AI 기술 개발에 참여할 수 있도록 준비하는 데 도움이 되는 기본 프로그램입니다. 머신 러닝을 업무에 적용하고, 기술 경력의 수준을 높이고, AI 세계에서 결정적인 단계를 밟을 수 있는 지식과 기술을 얻을 수 있는 경로를 제공합니다.
Register for this course and see more details by visiting:
OpenCourser.com/course/j6uyud/singyeongmang
What's inside
Syllabus
딥 러닝 소개
딥 러닝의 부상을 주도하는 주요 동향을 분석하고 오늘날 어디에 어떻게 적용되는지에 대한 예를 제시합니다.
신경망 기초
Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
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Syllabus
딥 러닝 소개
딥 러닝의 부상을 주도하는 주요 동향을 분석하고 오늘날 어디에 어떻게 적용되는지에 대한 예를 제시합니다.
신경망 기초
Set up a machine learning problem with a neural network mindset and use vectorization to speed up your models.
Read more
얕은 신경망
순방향 전파와 역전파를 사용하여 하나의 숨겨진 레이어로 신경망을 구축합니다.
심층 신경망
딥 러닝의 기반이 되는 주요 계산을 분석한 다음 이를 사용하여 컴퓨터 비전 작업을 위한 심층 신경망을 구축하고 훈련합니다.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Examines the primary trends that drive interest in deep learning, such as increased data availability, improved computing power, and the rise of deep learning frameworks and libraries
Provides hands-on experience with neural networks through interactive exercises and coding assignments
Taught by industry experts with proven experience in using deep learning to solve real-world problems
Suitable for beginners with no prior knowledge of deep learning who seek a comprehensive introduction to the field
Prerequisite knowledge in Python programming is required
Involves working with complex mathematical concepts and algorithms, which may be challenging for some learners
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신경망 및 딥 러닝.
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Career center
Learners who complete 신경망 및 딥 러닝 will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data to extract meaningful insights and patterns. The course on "Neural Networks and Deep Learning" provides a foundation in the fundamentals of deep learning and neural networks. By mastering these concepts, you'll be equipped with the knowledge and skills to develop and implement deep learning models for various applications, including data analysis and prediction.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers specialize in designing, developing, and deploying deep learning models for various applications. The course on "Neural Networks and Deep Learning" offers a comprehensive foundation in the field, providing you with the knowledge and skills to excel in this role. You'll master the techniques and algorithms used in deep learning, enabling you to contribute to the development of innovative deep learning solutions.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers develop, deploy, and maintain machine learning models. The course on "Neural Networks and Deep Learning" provides a deep understanding of the key concepts and algorithms used in deep learning, empowering you to excel in this role. You'll gain hands-on experience in building and training neural networks, enabling you to contribute effectively to the development of cutting-edge machine learning solutions.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to identify trends and patterns. The course on "Neural Networks and Deep Learning" provides a strong foundation in deep learning and neural networks. By gaining proficiency in these concepts, you'll be equipped to leverage deep learning techniques to extract valuable insights from large datasets, enhancing your capabilities as a Data Analyst.
Computer Vision Engineer
Computer Vision Engineer
Computer Vision Engineers develop and implement algorithms and systems for computer vision tasks such as image recognition and object detection. The course on "Neural Networks and Deep Learning" provides a thorough understanding of deep learning and neural networks, which are essential for success in this field. By mastering these concepts, you'll be able to develop and deploy state-of-the-art computer vision solutions.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. The course on "Neural Networks and Deep Learning" offers a solid foundation in the principles and applications of deep learning. By gaining expertise in these areas, you'll be equipped to incorporate deep learning models into software applications, enhancing their capabilities and functionality.
Natural Language Processing Engineer
Natural Language Processing Engineer
Natural Language Processing Engineers develop and implement algorithms and systems for natural language processing tasks such as text classification and machine translation. The course on "Neural Networks and Deep Learning" provides a deep dive into the fundamentals of deep learning and neural networks, which are widely used in natural language processing. By gaining proficiency in these areas, you'll be well-equipped to contribute to the development of innovative natural language processing solutions.
Research Scientist
Research Scientist
Research Scientists conduct research in various fields, including computer science, machine learning, and artificial intelligence. The course on "Neural Networks and Deep Learning" provides a comprehensive foundation in these areas, enabling you to pursue research in topics related to deep learning and neural networks. By mastering the concepts and techniques covered in the course, you'll enhance your capabilities as a Research Scientist and contribute to the advancement of the field.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use mathematical and statistical models to analyze financial data and make investment decisions. The course on "Neural Networks and Deep Learning" provides a strong foundation in deep learning and neural networks, which can be applied to quantitative finance tasks such as stock prediction and risk management. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative quantitative models that enhance investment strategies.
Business Intelligence Analyst
Business Intelligence Analyst
Business Intelligence Analysts collect, analyze, and interpret data to provide insights and recommendations for business decision-making. The course on "Neural Networks and Deep Learning" provides a strong foundation in deep learning and neural networks, which can be applied to business intelligence tasks such as customer segmentation and fraud detection. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative data-driven solutions that drive business value.
Healthcare Analyst
Healthcare Analyst
Healthcare Analysts analyze healthcare data to identify trends and patterns. The course on "Neural Networks and Deep Learning" provides a foundation in deep learning and neural networks, which can be applied to healthcare analysis tasks such as disease diagnosis and treatment planning. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative data-driven solutions that improve patient care.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use mathematical and analytical techniques to solve complex business problems. The course on "Neural Networks and Deep Learning" provides a foundation in deep learning and neural networks, which can be applied to operations research tasks such as supply chain optimization and resource allocation. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative solutions that improve operational efficiency.
Financial Analyst
Financial Analyst
Financial Analysts analyze financial data and make recommendations for investment decisions. The course on "Neural Networks and Deep Learning" provides a foundation in deep learning and neural networks, which can be applied to financial analysis tasks such as stock prediction and risk management. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative financial models that enhance investment strategies.
Marketing Analyst
Marketing Analyst
Marketing Analysts analyze marketing data to identify trends and patterns. The course on "Neural Networks and Deep Learning" provides a foundation in deep learning and neural networks, which can be applied to marketing analysis tasks such as customer segmentation and campaign optimization. By gaining proficiency in these areas, you'll be equipped to develop and implement innovative data-driven marketing strategies that drive business growth.
Product Manager
Product Manager
Product Managers are responsible for the development and management of products. The course on "Neural Networks and Deep Learning" provides a foundation in深 learning and neural networks, which are increasingly used in product development. By gaining proficiency in these areas, you'll be equipped to understand the potential applications of deep learning in product development and make informed decisions about incorporating these technologies into your products.
For more career information including salaries, visit:
OpenCourser.com/course/j6uyud/singyeongmang
Reading list
We've selected eight 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
신경망 및 딥 러닝.
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이 책은 딥 러닝의 포괄적인 개론을 제공합니다. 딥 러닝 모델의 이론적 기초와 다양한 응용 분야를 다룹니다.
이 책은 강화 학습의 기본 개념과 딥 러닝과의 통합을 다룹니다. 강화 학습 알고리즘을 구현하는 방법과 다양한 응용 분야를 설명합니다.
Save
이 책은 생성적 적대적 네트워크(GAN)의 이론과 구현에 중점을 둡니다. GAN을 사용하여 이미지 생성, 스타일 전송, 텍스트 생성과 같은 다양한 응용 분야를 다룹니다.
이 책은 자연어 처리에 딥 러닝을 적용하는 방법에 중점을 둡니다. 텍스트 분류, 기계 번역, 챗봇과 같은 다양한 자연어 처리 응용 분야를 다룹니다.
이 책은 컴퓨터 비전에 딥 러닝을 적용하는 방법에 중점을 둡니다. 이미지 분류, 객체 감지, 영상 세분화와 같은 다양한 컴퓨터 비전 응용 분야를 다룹니다.
이 책은 머신 러닝 알고리즘을 파이썬으로 구현하는 방법에 중점을 둡니다. 딥 러닝을 비롯한 다양한 머신 러닝 기술을 실제 사례와 함께 설명합니다.
이 책은 케라스 라이브러리를 사용하여 딥 러닝 모델을 구축하고 훈련하는 방법을 단계별로 안내합니다. 딥 러닝의 기본 개념과 다양한 모델 아키텍처를 다룹니다.
이 책은 딥 러닝을 이해하는 데 필요한 선형 대수, 확률론, 최적화 등의 수학적 기초를 다룹니다. 딥 러닝 모델의 수학적 원리를 엄밀하게 설명합니다.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/j6uyud/singyeongmang
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Effort
주당 5시간의 비율로 전문화 과정의 각 과정을 마치는 데 약 5주가 소요됩니다.
Level
Intermediate
Via
Coursera
Institution
DeepLearning.AI
Instructor
Younes Bensouda Mourri
Language
한국어
Transcripts
English
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Good to know
Examines the primary trends that drive interest in deep learning, such as increased data availability, improved computing power, and the rise of deep learning frameworks and libraries
Provides hands-on experience with neural networks through interactive exercises and coding assignments
Taught by industry experts with proven experience in using deep learning to solve real-world problems
Suitable for beginners with no prior knowledge of deep learning who seek a comprehensive introduction to the field
Prerequisite knowledge in Python programming is required
Involves working with complex mathematical concepts and algorithms, which may be challenging for some learners
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