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품목정보

품목정보
발행일 2021년 08월 25일
쪽수, 무게, 크기 628쪽 | 188*245*27mm
ISBN13 9791191600322
ISBN10 1191600327

카드 뉴스로 보는 책

책소개 책소개 보이기/감추기

목차 목차 보이기/감추기

PART V 기계학습
CHAPTER 19 견본에서 배우는 학습 3
19.1 학습의 여러 형태 ······················································································· 4
19.2 지도학습 ····································································································· 6
19.3 결정 트리의 학습 ····················································································· 11
19.4 모형 선택과 최적화 ················································································· 21
19.5 학습 이론 ································································································· 30
19.6 선형 회귀와 분류 ····················································································· 35
19.7 비매개변수 모형 ······················································································· 47
19.8 앙상블 학습 ······························································································ 59
19.9 기계학습 시스템 개발 ·············································································· 69
요약 ···································································································· 81
참고문헌 및 역사적 참고사항 ··························································· 82

CHAPTER 20 확률 모형의 학습 89
20.1 통계적 학습 ······························································································ 90
20.2 완전 데이터를 이용한 학습 ····································································· 93
20.3 은닉 변수가 있는 학습: EM 알고리즘 ················································· 109
요약 ·································································································· 119
참고문헌 및 역사적 참고사항 ························································· 120

CHAPTER 21 심층학습 125
21.1 단순 순방향 신경망 ··············································································· 127
21.2 심층학습을 위한 계산 그래프 ······························································· 133
21.3 합성곱 신경망 ························································································ 137
21.4 학습 알고리즘 ························································································ 144
21.5 일반화 ····································································································· 148
21.6 순환 신경망 ···························································································· 153
21.7 비지도학습과 전이학습 ·········································································· 157
21.8 응용 ········································································································ 165
요약 ·································································································· 168
참고문헌 및 역사적 참고사항 ························································· 168

CHAPTER 22 강화학습 173
22.1 보상 기반 학습 ······················································································ 173
22.2 수동 강화학습 ························································································ 176
22.3 능동 강화학습 ························································································ 183
22.4 강화학습의 일반화 ················································································· 191
22.5 정책 검색 ······························································································· 199
22.6 견습 학습과 역강화학습 ········································································ 202
22.7 강화학습의 응용 ····················································································· 206
요약 ·································································································· 209
참고문헌 및 역사적 참고사항 ························································· 211

PART VI 의사소통, 지각, 행동
CHAPTER 23 자연어 처리 217
23.1 언어 모형 ······························································································· 218
23.2 문법 ········································································································ 231
23.3 파싱 ········································································································ 233
23.4 증강 문법 ······························································································· 240
23.5 실제 자연어의 복잡한 사항들 ······························································· 246
23.6 자연어 처리 과제들 ··············································································· 250
요약 ·································································································· 252
참고문헌 및 역사적 참고사항 ························································· 253

CHAPTER 24 자연어 처리를 위한 심층학습 259
24.1 단어 내장 ······························································································· 260
24.2 NLP를 위한 순환 신경망 ······································································ 264
24.3 순차열 대 순차열 모형 ·········································································· 268
24.4 트랜스포머 구조 ····················································································· 274
24.5 사전훈련과 전이학습 ·············································································· 277
24.6 현황 ········································································································ 282
요약 ·································································································· 285
참고문헌 및 역사적 참고사항 ························································· 285

CHAPTER 25 컴퓨터 시각 289
25.1 소개 ········································································································ 289
25.2 이미지 형성 ···························································································· 291
25.3 단순 이미지 특징 ··················································································· 298
25.4 이미지 분류 ···························································································· 306
25.5 물체 검출 ······························································································· 311
25.6 3차원 세계 ····························································································· 314
25.7 컴퓨터 시각의 용도 ··············································································· 319
요약 ·································································································· 334
참고문헌 및 역사적 참고사항 ························································· 335

CHAPTER 26 로봇공학 341
26.1 로봇 ······································································································ 341
26.2 로봇 하드웨어 ······················································································ 342
26.3 로봇공학이 푸는 문제들 ······································································ 347
26.4 로봇 지각 ····························································································· 349
26.5 계획 수립과 제어 ················································································· 357
26.6 불확실한 운동의 계획 ·········································································· 378
26.7 로봇공학의 강화학습 ············································································ 381
26.8 인간과 로봇 ·························································································· 384
26.9 로봇공학의 또 다른 틀 ········································································ 394
26.10 응용 영역 ····························································································· 397
요약 ································································································ 400
참고문헌 및 역사적 참고사항 ······················································· 402

PART VII 결론
CHAPTER 27 인공지능의 철학, 윤리학, 안전 411
27.1 인공지능의 한계 ····················································································· 411
27.2 기계가 정말로 생각할 수 있을까? ······················································· 416
27.3 인공지능의 윤리 ····················································································· 418
요약 ·································································································· 443
참고문헌 및 역사적 참고사항 ························································· 443

CHAPTER 28 인공지능의 미래 451
28.1 인공지능의 구성요소 ·············································································· 452
28.2 인공지능 구조 ························································································ 459

APPENDIX A 수학적 배경 465
A.1 복잡도 분석과 O( ) 표기법 ··································································· 465
A.2 벡터, 행렬, 선형대수 ············································································· 468
A.3 확률분포 ································································································· 470
참고문헌 및 역사적 참고사항 ························································· 473

APPENDIX B 언어와 알고리즘에 관해 475
B.1 BNF를 이용한 언어의 정의 ·································································· 475
B.2 알고리즘 서술에 쓰이는 의사코드 ························································ 476
B.3 온라인 보조 자료 ··················································································· 478

? 참고문헌 ·················································· 479
? 찾아보기 ·················································· 537

저자 소개 (3명)

책 속으로 책속으로 보이기/감추기

기계가 지능적으로 행동할 수 있다는 가능성 자체에 반대하면서 약 인공지능을 비판한 사람들이 있었는데, 지금 시점에서 보면 그런 사람들은 선견지명이 부족했다고 할 수 있다. 예를 들어 사이먼 뉴컴(Simon Newcomb)은 1903년 10월에 “공중 비행은 인간이 절대로 감당할 수 없는 어려운 문제 중 하나이다”라고 썼지만, 불과 두 달 후에 라이트 형제가 키티호크의 들판에서 유인 동력 비행에 성공했다. 그러나, 최근 인공지능이 급격히 발전했지만 그렇다고 인공지능의 능력에 한계가 없음이 증명된 것은 아니다.
--- p.412

이러한 신뢰성 문제가 두드러지게 노출된 사례가 있다. 1986년 9월 26일 소비에트 미사일 장교 스타니슬라프 페트로프의 컴퓨터 디스플레이에 미사일 공격 경보가 떴다.
프로토콜에 따라 페트로프는 핵 무기 반격 절차를 시작해야 했지만, 그는 그 경보가 시스템의 버그 때문이라고 의심하고는 조사해 보았다. 그가 옳았으며, 덕분에 인류는 제3차 세계대전을 (가까스로) 피할 수 있었다. 그 과정에서 인간의 개입이 없었다면 어떤 일이 일어났을지 우리는 알지 못한다.
--- p.422

효용 함수가 외부효과(externality)들 때문에 잘못될 수도 있다. 외부효과는 경제학에서 측정 및 지불 대상 바깥의 요인을 가리키는 용어이다. 온실 가스를 외부효과로 간주하면 기업과 국가는 온실 가스를 배출해도 벌칙을 받지 않으며, 결과적으로 지구의 모든 사람이 고통을 받는다. 생태학자 개릿 하딘은 공유 자원의 남용을 가리켜 공유지의 비극(tragedy of the commons)이라고 불렀다. 외부효과를 내부화하면, 즉 외부효과를 효용 함수의 일부로 두면 비극을 완화할 수 있다. 탄소세의 도입이 그런 내부화의 예이다.
--- p.439

굿의 ‘지능 폭발’을 수학 교수이자 과학소설 작가 버너 빈지(Vernor Vinge)는 기술적 특이점(technological singularity)이라고 불렀다. 1993년에 그는 “30년 내로 인류는 초인적 지능을 만들어 낼 기술적 수단들을 갖출 것이다. 얼마 후에는 인류의 시대가 끝날 것이다”라고 썼다(Vinge, 1993). 2017년에 발명가이자 미래학자인 레이 커즈와일(Ray Kurzweil)은 2045년이면 특이점이 나타날 것이라고 예측했다. 24년 만에 미래 시점이 30년에서 28년으로 2년 앞당겨진 것이다. (이 속도로 간다면 336년밖에 남지 않았다!) 기술적 진보의 여러 측정치가 현재 지수적으로 증가한다는 빈지와 커즈와일의 지적은 사실이다.
--- p.441

출판사 리뷰 출판사 리뷰 보이기/감추기

2010년대 인공신경망의 부활과 심층학습의 눈부신 성과를 반영한 인공지능 연구의 결정판!

2016년에 나온 제3판 번역서(2009년에 출간된 원서 3판을 번역)는 최신 연구 반영에 한계가 있었으나, 이번 제4판 번역서는 최근 성과(자연어 이해, 로봇공학, 컴퓨터 시각에 심층학습이 끼친 영향, 강화학습을 로봇공학에 적용하는 방법, 기계학습, 인공지능 윤리 등)를 충실하게 반영한, 2020년에 출간된 원서를 옮긴 것이라 이 분야의 ‘좀 더 통합된 상’을 원하는 여러 독자의 갈증을 해소하는 데 큰 도움이 될 것입니다.

전체적으로, 책의 약 25%가 완전히 새로운 내용이고 나머지 75%도 이 분야의 좀 더 통합된 상을 제시하기 위해 크게 변경되었으며, 이번 판에서 인용한 문헌의 22%는 2010년 이후에 출판된 것들입니다.

제4판에서 새로운 점들
■ 사람이 손으로 짜는 지식 공학보다는 기계학습에 좀 더 무게를 실었다. 기계학습은 가용 데이터와 컴퓨팅 자원이 증가하고 새로운 알고리즘들이 등장한 덕분에 큰 성공을 거두고 있다.
■ 심층학습, 확률적 프로그래밍, 다중 에이전트 시스템을 각각 개별적인 장(챕터)으로 두어서 좀 더 자세히 다룬다.
■ 자연어 이해, 로봇공학, 컴퓨터 시각에 관한 장들을 심층학습이 끼친 영향을 반영해서 수정했다.
■ 로봇공학 장에 사람과 상호작용하는 로봇에 관한 내용과 강화학습을 로봇공학에 적용하는 방법에 관한 내용이 추가되었다.
■ 이전에는 인공지능의 목표를 사람이 구체적인 효용 정보(목적함수)를 제공한다는 가정하에서 기대 효용을 최대화하려는 시스템을 만드는 것이라고 정의했다. 그러나 이번 판에서는 목적함수가 고정되어 있으며 인공지능 시스템이 목적함수를 알고 있다고 가정하지 않는다. 대신, 시스템은 자신이 봉사하는 인간의 진짜 목적이 무엇인지 확실하게 알지 못할 수 있다고 가정한다. 시스템은 반드시 자신이 무엇을 최대화할 것인지를 배워야 하며, 목적에 관해 불확실성이 존재하더라도 적절히 작동해야 한다.
■ 인공지능이 사회에 미치는 영향을 좀 더 자세하게 다루었다. 여기에는 윤리, 공정성, 신뢰, 안정성에 관한 핵심적인 문제들을 고찰한다.
■ 각 장 끝의 연습문제들을 온라인 사이트로 옮겼다. 덕분에 강사들의 요구와 이 분야 및 인공지능 관련 소프트웨어 도구의 발전에 맞게 연습문제들을 계속 추가, 갱신, 개선할 수 있게 되었다.

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