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Pro deep learning with TensorFlow [electronic resource] : a mathematical approach to advanced artificial intelligence in Python

Pro deep learning with TensorFlow [electronic resource] : a mathematical approach to advanced artificial intelligence in Python

자료유형
E-Book(소장)
개인저자
Pattanayak, Santanu.
서명 / 저자사항
Pro deep learning with TensorFlow [electronic resource] : a mathematical approach to advanced artificial intelligence in Python / Santanu Pattanayak.
발행사항
Berkeley, CA :   Apress,   c2017.  
형태사항
1 online resource (xxi, 398 p.) : ill. (some col.).
기타형태 저록
Print version:   Pattanayak, Santanu.   Pro deep learning with TensorFlow   9781484230954   (211009)000045936230  
ISBN
9781484230954 9781484230961 (eBook)
요약
Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.  What You'll Learn: Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow.
일반주기
Title from e-Book title page.  
내용주기
Chapter 1: Mathematical Foundations -- Chapter 2: Introduction to Deep Learning Concepts and TensorFlow -- Chapter 3: Convolutional Neural Networks -- Chapter 4: Natural Language Processing Using Recursive Neural Networks -- Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto Encoders -- Chapter 6: Advanced Neural Networks.
서지주기
Includes bibliographical references and index.
이용가능한 다른형태자료
Issued also as a book.  
일반주제명
Machine learning. Artificial intelligence.
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URL
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245 1 0 ▼a Pro deep learning with TensorFlow ▼h [electronic resource] : ▼b a mathematical approach to advanced artificial intelligence in Python / ▼c Santanu Pattanayak.
260 ▼a Berkeley, CA : ▼b Apress, ▼c c2017.
300 ▼a 1 online resource (xxi, 398 p.) : ▼b ill. (some col.).
500 ▼a Title from e-Book title page.
504 ▼a Includes bibliographical references and index.
505 0 ▼a Chapter 1: Mathematical Foundations -- Chapter 2: Introduction to Deep Learning Concepts and TensorFlow -- Chapter 3: Convolutional Neural Networks -- Chapter 4: Natural Language Processing Using Recursive Neural Networks -- Chapter 5: Unsupervised Learning with Restricted Boltzmann Machines and Auto Encoders -- Chapter 6: Advanced Neural Networks.
520 ▼a Deploy deep learning solutions in production with ease using TensorFlow. You'll also develop the mathematical understanding and intuition required to invent new deep learning architectures and solutions on your own. Pro Deep Learning with TensorFlow provides practical, hands-on expertise so you can learn deep learning from scratch and deploy meaningful deep learning solutions. This book will allow you to get up to speed quickly using TensorFlow and to optimize different deep learning architectures. All of the practical aspects of deep learning that are relevant in any industry are emphasized in this book. You will be able to use the prototypes demonstrated to build new deep learning applications. The code presented in the book is available in the form of iPython notebooks and scripts which allow you to try out examples and extend them in interesting ways. You will be equipped with the mathematical foundation and scientific knowledge to pursue research in this field and give back to the community.  What You'll Learn: Understand full stack deep learning using TensorFlow and gain a solid mathematical foundation for deep learning Deploy complex deep learning solutions in production using TensorFlow Carry out research on deep learning and perform experiments using TensorFlow.
530 ▼a Issued also as a book.
538 ▼a Mode of access: World Wide Web.
630 0 0 ▼a TensorFlow (Electronic resource).
650 0 ▼a Machine learning.
650 0 ▼a Artificial intelligence.
776 0 8 ▼i Print version: ▼a Pattanayak, Santanu. ▼t Pro deep learning with TensorFlow ▼z 9781484230954 ▼w (211009)000045936230
856 4 0 ▼u https://oca.korea.ac.kr/link.n2s?url=https://doi.org/10.1007/978-1-4842-3096-1
945 ▼a KLPA
991 ▼a E-Book(소장)

소장정보

No. 소장처 청구기호 등록번호 도서상태 반납예정일 예약 서비스
No. 1 소장처 중앙도서관/e-Book 컬렉션/ 청구기호 CR 006.31 등록번호 E14014738 도서상태 대출불가(열람가능) 반납예정일 예약 서비스 M

컨텐츠정보

저자소개

산타누 파타나야크(지은이)

현재 GE에서 수석 데이터 과학자로 근무하고 있다. 데이터 분석 및 데이터 과학 분야에서 쌓은 6년의 경력을 비롯해 총 10년 동안 이 분야에서 근무했다. 또한 개발과 데이터베이스 기술 분야도 경험했다. GE에 입사하기 전에는 RBS, 캡게미니(Capgemini), IBM 등의 회사에서 근무했다. 인도의 콜카타 자다브푸르 대학에서 전기공학 학사를 받았고, 열렬한 수학 애호가다. 현재는 하이데라바드 소재 인도 기술연구소(IIT)에서 데이터 과학 석사 과정을 밟고 있다. 데이터 과학 해커톤(hackathon)과 캐글(Kaggle) 경연 대회에 참가하는 데 많은 시간을 투자하고 있으며, 전 세계 500등 이내에 위치한다. 인도의 웨스트 벵갈에서 태어나고 자랐으며, 현재 인도 벵갈루루에서 아내와 함께 살고 있다. http://www.santanupattanayak.com/에서 최근 활동을 확인할 수 있다.

정보제공 : Aladin

목차

Intro -- Contents -- About the Author -- About the Technical Reviewer -- Acknowledgments -- Introduction -- Chapter 1: Mathematical Foundations -- Linear Algebra -- Vector -- Scalar -- Matrix -- Tensor -- Matrix Operations and Manipulations -- Addition of Two Matrices -- Subtraction of Two Matrices -- Product of Two Matrices -- Transpose of a Matrix -- Dot Product of Two Vectors -- Matrix Working on a Vector -- Linear Independence of Vectors -- Rank of a Matrix -- Identity Matrix or Operator -- Determinant of a Matrix -- Interpretation of Determinant -- Inverse of a Matrix -- Norm of a Vector -- Pseudo Inverse of a Matrix -- Unit Vector in the Direction of a Specific Vector -- Projection of a Vector in the Direction of Another Vector -- Eigen Vectors -- Characteristic Equation of a Matrix -- Power Iteration Method for Computing Eigen Vector -- Calculus -- Differentiation -- Gradient of a Function -- Successive Partial Derivatives -- Hessian Matrix of a Function -- Maxima and Minima of Functions -- Rules for Maxima and Minima for a Univariate Function -- Local Minima and Global Minima -- Positive Semi-Definite and Positive Definite -- Convex Set -- Convex Function -- Non-convex Function -- Multivariate Convex and Non-convex Functions Examples -- Taylor Series -- Probability -- Unions, Intersection, and Conditional Probability -- Chain Rule of Probability for Intersection of Event -- Mutually Exclusive Events -- Independence of Events -- Conditional Independence of Events -- Bayes Rule -- Probability Mass Function -- Probability Density Function -- Expectation of a Random Variable -- Variance of a Random Variable -- Skewness and Kurtosis -- Covariance -- Correlation Coefficient -- Some Common Probability Distribution -- Uniform Distribution -- Normal Distribution -- Multivariate Normal Distribution -- Bernoulli Distribution -- Binomial Distribution -- Poisson Distribution -- Likelihood Function -- Maximum Likelihood Estimate -- Hypothesis Testing and p Value -- Formulation of Machine-Learning Algorithm and Optimization Techniques -- Supervised Learning -- Linear Regression as a Supervised Learning Method -- Linear Regression Through Vector Space Approach -- Classification -- Hyperplanes and Linear Classifiers -- Unsupervised Learning -- Optimization Techniques for Machine Learning -- Gradient Descent -- Gradient Descent for a Multivariate Cost Function -- Contour Plot and Contour Lines -- Steepest Descent -- Stochastic Gradient Descent -- Newton’s Method -- Linear Curve -- Negative Curvature -- Positive Curvature -- Constrained Optimization Problem -- A Few Important Topics in Machine Learning -- Dimensionality Reduction Methods -- Principal Component Analysis -- When Will PCA Be Useful in Data Reduction? -- How Do You Know How Much Variance Is Retained by the Selected Principal Components? -- Singular Value Decomposition -- Regularization -- Regularization Viewed as a Constraint Optimization Problem -- Summary -- Chapter 2: Introduction to Deep-Le.

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