단행본
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- 발행사항
- Sebastopol, California : O'Reilly Media, 2017
- 형태사항
- XX, 549p. : illustrations ; 24cm
- 서지주기
- Includes index
- 키워드
- TensorFlow
소장정보
| 위치 | 등록번호 | 청구기호 / 출력 | 상태 | 반납예정일 |
|---|---|---|---|---|
이용 가능 (1) | ||||
| 자료실 | E206986 | 대출가능 | - | |
이용 가능 (1)
- 등록번호
- E206986
- 상태/반납예정일
- 대출가능
- -
- 위치/청구기호(출력)
- 자료실
책 소개
Learn how to use Machine Learning in your projects using actual production-ready python frameworks, namely Python Scikit-Learn (for most code), TensorFlow (for neural nets) and PyBrain (for reinforcement learning).
This book favors a practical approach using real-life production-ready tools, and it builds up instincts quickly using concrete examples and minimal theory (avoiding spending too much time on excessive theory and unnecessary details of every algorithm).
목차
Preface
PartⅠ. The Fundamentals of Machine Learning
Chapter 1 The Machine Learning Landscape
Chapter 2 End-to-End Machine Learning Project
Chapter 3 Classification
Chapter 4 Training Models
Chapter 5 Support Vector Machines
Chapter 6 Decision Trees
Chapter 7 Ensemble Learning and Random Forests
Chapter 8 Dimensionality Reduction
Part II. Neural Networks and Deep Learning
Chapter 9 Up and Running with TensorFlow
Chapter 10 Introduction to Artificial Neural Networks
Chapter 11 Training Deep Neural Nets
Chapter 12 Distributing TensorFlow Across Devices and Servers
Chapter 13 Convolutional Neural Networks
Chapter 14 Recurrent Neural Networks
Chapter 15 Autoencoders
Chapter 16 Reinforcement Learning
Appendix
A. Exercise Solutions
B. Machine Learning Project Checklist
C. SVM Dual Problem
D. Autodiff
E. Other Popular ANN Architectures
INDEX