عنوان

An introduction to machine learning /

(Number (ISBN

3030157288

(Number (ISBN

3030157296

(Number (ISBN

303015730X

(Number (ISBN

3030157318

(Number (ISBN

9783030157289

(Number (ISBN

9783030157296

(Number (ISBN

9783030157302

(Number (ISBN

9783030157319

Erroneous ISBN

9783030157289

Title Proper

An introduction to machine learning /

General Material Designation

[Book]

First Statement of Responsibility

Gopinath Rebala, Ajay Ravi and Sanjay Churiwala.

Place of Publication, Distribution, etc.

Cham :

Name of Publisher, Distributor, etc.

Springer,

Date of Publication, Distribution, etc.

[2019]

Date of Publication, Distribution, etc.

©2019

Specific Material Designation and Extent of Item

1 online resource

Text of Note

Includes bibliographical references and index.

Text of Note

Intro; Preface; Reading the Book; Acknowledgments; Contents; List of Figures; List of Tables; Chapter 1: Machine Learning Definition and Basics; 1.1 Introduction; 1.1.1 Resurgence of ML; 1.1.2 Relation with Artificial Intelligence (AI); 1.1.3 Machine Learning Problems; 1.2 Matrices; 1.2.1 Vector and Tensors; 1.2.2 Matrix Addition (or Subtraction); 1.2.3 Matrix Transpose; 1.2.4 Matrix Multiplication; 1.2.4.1 Multiplying with a Scalar; 1.2.4.2 Multiplying with Another Matrix; 1.2.4.3 Multiplying with a Vector; 1.2.5 Identity Matrix; 1.2.6 Matrix Inversion; 1.2.7 Solving Equations Using Matrices

Text of Note

1.3 Numerical Methods1.4 Probability and Statistics; 1.4.1 Sampling the Distribution; 1.4.2 Random Variables; 1.4.3 Expectation; 1.4.4 Conditional Probability and Distribution; 1.4.5 Maximum Likelihood; 1.5 Linear Algebra; 1.6 Differential Calculus; 1.6.1 Functions; 1.6.2 Slope; 1.7 Computer Architecture; 1.8 Next Steps; Chapter 2: Learning Models; 2.1 Supervised Learning; 2.1.1 Classification Problem; 2.1.2 Regression Problem; 2.2 Unsupervised Learning; 2.3 Semi-supervised Learning; 2.4 Reinforcement Learning; Chapter 3: Regressions; 3.1 Introduction; 3.2 The Model; 3.3 Problem Formulation

Text of Note

3.4 Linear Regression3.4.1 Normal Method; 3.4.2 Gradient Descent Method; 3.4.2.1 Determine the Slope at Any Given Point; 3.4.2.2 Initial Value; 3.4.2.3 Correction; 3.4.2.4 Learning Rate; 3.4.2.5 Convergence; 3.4.2.6 Alternate Method for Computing Slope; 3.4.2.7 Putting Gradient Descent in Practice; 3.4.3 Normal Equation Method vs Gradient Descent Method; 3.5 Logistic Regression; 3.5.1 Sigmoid Function; 3.5.2 Cost Function; 3.5.3 Gradient Descent; 3.6 Next Steps; 3.7 Key Takeaways; Chapter 4: Improving Further; 4.1 Nonlinear Contribution; 4.2 Feature Scaling

Text of Note

4.5.2.1 Basic Approach for SoftMax4.5.2.2 Loss Function; 4.6 Key Takeaways and Next Steps; Chapter 5: Classification; 5.1 Decision Boundary; 5.1.1 Nonlinear Decision Boundary; 5.2 Skewed Class; 5.2.1 Optimizing Precision vs Recall; 5.2.2 Single Metric; 5.3 Naïve Bayes ́Algorithm; 5.4 Support Vector Machines; 5.4.1 Kernel Selection; Chapter 6: Clustering; 6.1 K-Means; 6.1.1 Basic Algorithm; 6.1.2 Distance Calculation; 6.1.3 Algorithm Pseudo Code; 6.1.4 Cost Function; 6.1.5 Choice of Initial Random Centers; 6.1.6 Number of Clusters; 6.2 K-Nearest Neighbor (KNN); 6.2.1 Weight Consideration

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Text of Note

Just like electricity, Machine Learning will revolutionize our life in many ways - some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with.

Source for Acquisition/Subscription Address

Springer Nature

Stock Number

com.springer.onix.9783030157296

International Standard Book Number

9783030157289

International Standard Book Number

9783030157302

International Standard Book Number

9783030157319

Machine learning.

COMPUTERS-- General.

Machine learning.

COM-- 000000

TJFC

TJFC

Number

Edition

Class number

Rebala, Gopinath

Churiwala, Sanjay

Ravi, Ajay

Date of Transaction

20200823084710.0

Cataloguing Rules (Descriptive Conventions))

pn

Electronic name

[Book]

Y