시퀀스 모델 from Coursera

딥 러닝 전문화의 다섯 번째 과정에서는 시퀀스 모델과 음성 인식, 음악 합성, 챗봇, 기계 번역, 자연어 처리(NLP) 등과 같은 흥미로운 애플리케이션에 익숙해질 것입니다.

이 과정을 이수하면 순환 신경망(RNN)과 GRU 및 LSTM과 같이 일반적으로 사용되는 변형을 구축 및 훈련하고, RNN을 문자 수준의 언어 모델링에 적용하며, 자연어 처리 및 단어 임베딩에 대한 경험을 얻을 수 있으며, HuggingFace 토크나이저 및 트랜스포머 모델을 사용하여 NER 및 질문에 답하기 같은 다양한 NLP 작업을 해결합니다.

딥 러닝 전문화 과정은 딥 러닝의 기능, 과제 및 결과를 이해하고 최첨단 AI 기술의 개발에 참여할 준비를 하는 데 도움이 되는 기본 프로그램입니다. 경력을 쌓기 위한 지식과 기술을 습득할 수 있도록 도와줌으로써 AI 세계에서 최종적인 단계를 맡을 수 있는 길을 제공합니다.

What's inside

Syllabus

순환 신경망

LSTM, GRU 및 양방향 RNN을 포함하여 시계열 데이터에 뛰어난 성능을 보이는 모델 유형 및 여러 변형 및 순환 신경망 확인.

자연어 처리 및 단어 임베딩

자연어 처리의 딥 러닝는 가장 강력한 조합입니다. 단어 벡터 표현 및 임베딩 레이어를 사용하여 감정 분석, 명명된 엔터티 인식 및 신경망 기계 번역을 비롯한 다양한 애플리케이션에서 뛰어난 성능으로 순환 신경망을 훈련합니다.

시퀀스 모델 및 주의 메커니즘

입력 시퀀스가 주어졌을 때 모델이 주의를 집중할 위치를 결정하는 데 도움이 되는 알고리즘인 주의 메커니즘을 사용하여 시퀀스 모델을 보강합니다. 그런 다음 음성 인식과 오디오 데이터를 처리하는 방법을 살펴봅니다.

트랜스포머 네트워크

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Examines various types of sequence models, their performance with time series data, and their variants, including LSTM, GRU, and bidirectional RNNs

Provides hands-on experience with applying RNNs for text-based applications such as sentiment analysis, named entity recognition, and neural machine translation

Introduces attention mechanisms, a powerful technique for enhancing sequence models

Covers transformer networks, a cutting-edge type of sequence model that has revolutionized natural language processing

Can be complemented by other courses in deep learning to build a strong foundation in the field

Requires basic knowledge of deep learning and neural networks

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in 시퀀스 모델 with these activities:

NLP 분야의 멘토 찾기

Show steps

NLP 분야에 대한 지도와 지원을 제공하여 학습 경험을 향상시킵니다.

Show steps

LinkedIn, Meetup 등의 플랫폼에서 멘토 탐색하기
공통 관심사와 전문성을 공유하는 멘토 찾기
멘토와 정기적인 회의 일정 잡기

Review RNNs

Show steps

This course builds upon your understanding of RNNs, so it's important to review the basics before starting. This will help you follow along with the lectures and discussions more easily.

Show steps

Go over your notes or textbook chapters on RNNs.
Watch online videos or tutorials on RNNs.
Practice implementing simple RNNs in a programming language.

RNN 문제 풀이 연습하기

Show steps

개념 적용을 강화하고 결함 있는 지역 파악에 도움이 됩니다.

Browse courses on RNN

Show steps

문제 모음 찾기
문제 풀기
해답 확인 및 오류 분석하기

Four other activities

Expand to see all activities and additional details

Show all seven activities

NLP 관련 워크숍이나 밋업 참석하기

Show steps

실무자로부터 최신 트렌드와 기술을 배우는데 도움이 됩니다.

Browse courses on NLP

Show steps

관련 워크숍이나 밋업 찾기
등록하고 참석하기
전문가와 네트워킹하고 질문하기

TensorFlow 순환 신경망 자습서 탐구하기

Show steps

RNN 구현 및 훈련에 대한 실습적 이해를 제공하여 코스 개념을 보완합니다.

Browse courses on TensorFlow

Show steps

TensorFlow 설치 및 설정하기
튜토리얼 자료 다운로드하기
튜토리얼 단계 따르기 및 예제 실행하기

반복적인 시퀀스 예측 연습하기

Show steps

순환 신경망과 시퀀스 모델을 이해하는 데 도움이 됩니다.

Browse courses on RNN

Show steps

다양한 시퀀스 데이터 세트에서 예측 작업 수행하기
RNN과 LSTM, GRU와 같은 다양한 모델 탐구하기
모델 성능 평가하고 최적의 하이퍼파라미터 조정하기

음성 또는 텍스트 데이터를 사용한 NLP 프로젝트 구축하기

Show steps

시퀀스 모델과 NLP를 현실 세계 문제에 적용하는 데 도움이 됩니다.

Browse courses on NLP

Show steps

프로젝트 범위와 목표 정의하기
데이터 수집 및 처리하기
적절한 모델 선택 및 훈련하기
모델 평가 및 배포하기

Career center

Learners who complete 시퀀스 모델 will develop knowledge and skills that may be useful to these careers:

Natural Language Processing Engineer

Natural Language Processing Engineers develop and apply natural language processing algorithms to solve problems in the field of language. This course would be a useful step towards becoming a Natural Language Processing Engineer, as it covers topics such as recurrent neural networks, sequence models, and natural language processing.

See salaries and explore the career path for Natural Language Processing Engineer

Computational Linguist

Computational Linguists develop and apply natural language processing algorithms to solve problems in the field of language. This course would be useful for Computational Linguists because it covers topics such as recurrent neural networks, sequence models, and natural language processing.

See salaries and explore the career path for Computational Linguist

Artificial Intelligence Engineer

Artificial Intelligence Engineers design, develop, and maintain artificial intelligence systems. Taking this course would be a useful step towards becoming an Artificial Intelligence Engineer, since deep learning is a type of artificial intelligence.

See salaries and explore the career path for Artificial Intelligence Engineer

Machine Learning Engineer

Machine Learning Engineers are responsible for developing and deploying machine learning models. They work on tasks such as building data pipelines, training models, and evaluating performance. This course would help someone who wants to be a Machine Learning Engineer because it covers topics such as recurrent neural networks, sequence models, and natural language processing.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist blends fields of mathematics, computer science, and statistics to extract insights from data. As deep learning is a subset of machine learning and machine learning is a subset of artificial intelligence, taking this course would help build a foundation for a career as a Data Scientist. This course would be especially helpful since Data Scientists need to be able to use deep learning models for tasks like natural language processing, computer vision, and speech recognition.

See salaries and explore the career path for Data Scientist

Algorithm Engineer

Algorithm Engineers design, develop, test, and deploy algorithms to solve business problems. Taking this course would be a good step towards an Algorithm Engineer position since deep learning is a type of algorithm used to make computers do complex tasks. Deep learning models are also commonly used in machine learning and artificial intelligence, which are both fields relevant to Algorithm Engineers.

See salaries and explore the career path for Algorithm Engineer

Research Scientist

A Research Scientist conducts research to advance scientific knowledge and technology. Taking this course may be helpful for a Research Scientist that is working in a field that uses deep learning, such as natural language processing, computer vision, or speech recognition.

See salaries and explore the career path for Research Scientist

Data Analyst

Data Analysts analyze data to extract insights and inform decision-making. This course may be helpful for Data Analysts who want to learn more about deep learning, as deep learning is used for tasks like natural language processing, computer vision, and speech recognition.

See salaries and explore the career path for Data Analyst

Software Engineer

A Software Engineer designs, develops, and maintains software applications. Taking this course would be a useful step towards becoming a Software Engineer, since deep learning is used in many software applications, such as natural language processing, computer vision, and speech recognition.

See salaries and explore the career path for Software Engineer

Product Manager

Product Managers manage the development and launch of new products. This course may be helpful for Product Managers who want to learn more about how deep learning can be used to develop innovative new products.

See salaries and explore the career path for Product Manager

Business Analyst

Business Analysts analyze business processes and make recommendations for improvement. This course may be helpful for Business Analysts who want to learn more about how deep learning can be used to improve business processes.

See salaries and explore the career path for Business Analyst

Project Manager

Project Managers plan, execute, and close projects. This course may be helpful for Project Managers who are working on projects that involve deep learning.

See salaries and explore the career path for Project Manager

Sales Engineer

Sales Engineers sell technical products and services. This course may be helpful for Sales Engineers who want to learn more about deep learning so that they can sell deep learning-based products and services.

See salaries and explore the career path for Sales Engineer

Technical Writer

Technical Writers create technical documentation for software and other products. This course may be helpful for Technical Writers who want to learn more about deep learning so that they can write documentation for deep learning-based products.

See salaries and explore the career path for Technical Writer

Marketing Manager

Marketing Managers plan and execute marketing campaigns. This course may be helpful for Marketing Managers who want to learn more about how deep learning can be used to improve marketing campaigns.

See salaries and explore the career path for Marketing Manager

시퀀스 모델

What's inside

Syllabus

Good to know

Save this course

Activities

Career center

Reading list

Share

Similar courses