Sparse Matrix

Sparse Matrix, available at Free, has an average rating of 3.78, with 12 lectures, based on 9 reviews, and has 691 subscribers.

You will learn about Sparse matrix representation in RCV format and CSR format Applications of Sparse Matrix Operations on Sparse Matrix Implementations of Sparse matrix in Java This course is ideal for individuals who are Under graduate & Post graduate or Research Scholars It is particularly useful for Under graduate & Post graduate or Research Scholars.

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Summary

Title: Sparse Matrix

Price: Free

Average Rating: 3.78

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Sparse matrix representation in RCV format and CSR format
Applications of Sparse Matrix
Operations on Sparse Matrix
Implementations of Sparse matrix in Java

Who Should Attend

Under graduate & Post graduate
Research Scholars

Target Audiences

Under graduate & Post graduate
Research Scholars

This course discusses about sparse matrices, its representations & operations and applications. Sparse matrix is one of the linear data structures to deal with non zero elements. If a matrix consists of more number of zero elements, storing those zeros and processing them would decrease computational efficiency. Use of sparse Matrix improves computational efficiency in terms of storage space and computational time. This course would be more useful to undergraduate, postgraduate students and research scholars who work with large data sets embedded with more zero elements.

This course teaches, the ways to represent sparse matrix such as Triples format( RCV format) and Compressed Sparse Row format (CSR). The implementations in Java to convert sparse Matrix to RCV & CSR Formats are explained in interactive mode. This course also covers applying sparse matrix in one of the machine learning applications “sentiment analysis on products”. Sparse matrix operations are explained in step by step with suitable examples. Operations included are Addition, Transpose and Multiplication. All the operations are explained with step by step process of rows(Row ,Column and Value) of input matrices. Movement of position from one row of a matrix to another row is explained in a simple way. The entire course is taught with Java coding. Learners feel more easier to understand the coding and influences to write their own coding.

Course Curriculum

Chapter 1: SPARSE MATRIX

Lecture 1: Introduction

Lecture 2: Application of Sparse Matrix

Lecture 3: Sparse Matrix Representation in Triples format

Lecture 4: Sparse Matrix Representation in CSR format

Lecture 5: Operations on Sparse Matrix-Addition

Lecture 6: Operations on Sparse Matrix-Transpose

Lecture 7: Operations on Sparse Matrix-Multiplication

Chapter 2: Implementations of Sparse Matrix

Lecture 1: Implementation of Sparse Matrix into Triplet format(Row, Column, Value)

Lecture 2: Implementation of Sparse Matrix into Row Column Value Format

Lecture 3: Implementation of CSR Format

Lecture 4: Implementation of Sparse Matrix Addition

Lecture 5: Implementation of Sparse Matrix Addition -Program

Instructors

Dr. S. Anusuya Venkatesan
Professor

Rating Distribution

1 stars: 0 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 3 votes
5 stars: 2 votes

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