Singular Value Decomposition (SVD) is one of the most general-purpose tools in numerical linear algebra for data processing. The data reduction mechanism of SVD allows us to identify the key features of big data, that are necessary for analyzing, understanding, and describing the features.
In this repository, we have primarily focused on compressing images with high resolution using SVD.
-
Books -
Mathematics Course Material -
Statistics Course Material -
Machine Learning Course Material -
Previous Year Solved Papers for ISI and TIFR Entrance Exams
- Challenging Mathematical Problems for BS Entrances
- Olympiad Mathematics
- Solving Mathematical Problems for MS Entrances
- Differential Calculus
- Linear Algebra Part 1, Part 2
- Matrix Algebra
- Mathematical Analysis
- Numerical Analysis
- Operations Research
- Real Analysis
- Sequence and Series of Real Numbers
- Introduction to Probability
- Probability Theory Part 1, Part 2, Part 3, Part 4
- Descriptive Statistics Part 1, Part 2
- Population Statistics
- Time Series Analysis
- Economic Statistics Part 1, Part 2
- Statistical Quality Control Part 1, Part 2
- Official Statistics
- Transformations of Random Variables
- Large Sample Theory
- Sampling Distribution
- Statistical Inference Part 1, Part 2, Part 3,
- Nonparametric Inference
- Bayesian Inference
- ANOVA and Design of Experiment
- Sample Survey
- Multivariate Statistics
- Practical on Statistics
- Statistics for Decision Making
- Reliability Theory
- Stochastic Process
- Regression Techniques
- Industrial Experimentation