Personal academic website.
- References and Pointers Basics
- Passing and Returning References and Pointers
- Rvalue and Rvalue References
- Function Basics
- Inline and Constexpr Functions
- Functions Overloading and Matching
- Argument Type Conversions
- Pointers to Functions
- Between Functions Within A File
- Between Files
- Between Threads
- Between Processes
- Between Local Devices
- Between Remote Devices
- Probability vs Statistics
- Shakespear's New Poem
- Some Common Discrete Distributions
- Some Common Continuous Distributions
- Statistical Quantities
- Order Statistics
- Multivariate Normal Distributions
- Conditional Distributions and Expectation
- Problem Set [01] - Probabilities
- Parameter Point Estimation
- Evaluation of Point Estimation
- Parameter Interval Estimation
- Problem Set [02] - Parameter Estimation
- Parameter Hypothesis Test
- t Test
- Chi-Squared Test
- Analysis of Variance
- Summary of Statistical Tests
- Python [01] - Data Representation
- Python [02] - t Test & F Test
- Python [03] - Chi-Squared Test
- Experimental Design
- Monte Carlo
- Variance Reducing Techniques
- From Uniform to General Distributions
- Problem Set [03] - Monte Carlo
- Unitary Regression Model
- Multiple Regression Model
- Factor and Principle Component Analysis
- Clustering Analysis
- Data Types [01]: Python Built-In Types
- Data Types [02]: Numpy Array Basics
- Data Types [03]: Pandas DataFrame Basics
- Data Cleaning [01] - Handling Missing Data
- Data Cleaning [02] - Data Transformation
- Data Cleaning [03] - String Manipulation
- Data Cleaning [04] - Regular Expression Examples
- Data Wrangling [01] - Multi-Indexing
- Data Wrangling [02] - Merging DataFrames
- Data Wrangling [03] - Reshaping and Pivot Tables
- Data Aggregation and Grouping [01] - GroupBy Method
- Data Aggregation and Grouping [02] - Data Aggregation
- Data Visualization [01]: Matplotlib Basics
- Data Visualization [02]: Pandas and Seaborn Basics
- Data Modeling [01] - Patsy and Statsmodels
- Data Modeling [02] - Scikit-Learn
- Data Modeling [03] - Pytorch
- Data Modeling [04] - Keras and Tensorflow