This repo houses code for implementing various mathematical concepts in Python.
- ✅ classical_statistics.ipynb - Exploring Classical Statistics with Practical Python Implementation
Application | Topic | Status |
---|---|---|
Location | Confidence Limits for the Mean | ✅ |
Location | One sample t-Test | ✅ |
Location | Two sample t-Test for Equal Means | ✅ |
Location | One-Way ANOVA test | ✅ |
Scale or variability or spread | Bartlett Test | ✅ |
Scale or variability or spread | Levene Test | ✅ |
Skewness and Kurtosis | Skewness and Kurtosis | ✅ |
Distributional Measures | Quantile-Quantile Plot | ✅ |
Distributional Measures | Shapiro-Wilk test | ✅ |
Distributional Measures | D’Agostino’s K^2 Test | ✅ |
Distributional Measures | Anderson Darling Test | ✅ |
Distributional Measures | Chi-Squared Goodness of Fit Test | ✅ |
Distributional Measures | Kolmogorov-Smirnov Test | ✅ |
Outlier Detection | Isolation Forest | ✅ |
Outlier Detection | Grubbs Test | ✅ |
Outlier Detection | Box plot and scatter plot | ✅ |
Outlier Detection | Z-Score | ✅ |
Outlier Detection | IQR Score | ✅ |
Outlier Detection | Generalized Extreme Deviate Test | ✅ |
Outlier Detection | Distance-based outlier detection | ✅ |
Outlier Detection | Outlier detection based on polynomial fit | ✅ |
Feature Selection | Univariate Selection | ✅ |
Feature Selection | Recursive Feature Elimination | ✅ |
Feature Selection | Principal Component Analysis | ✅ |
Feature Selection | Linear Discriminant Analysis | ✅ |
Feature Selection | Feature Importance | ✅ |
- ✅ vector_operations_and_norms - Finding the
$L_p$ norm of a vector
Topic | Status |
---|---|
Quick intro to Vectors | ✅ |
Element-wise vector operations | ✅ |
Scalar broadcasting in Vector operations | ✅ |
Dot, Cross, Scalar Triple and Vector Triple Products | ✅ |
|
✅ |
- ✅ activation_functions_in_ML.ipynb - Exploring Activation Functions in Machine Learning
Topic | Status |
---|---|
Sigmoid Function | ✅ |
tanh Function | ✅ |
Rectified Linear Unit (ReLU) Function | ✅ |
Leaky Rectified Linear Unit (LeakyReLU) Function | ✅ |
Parametric Rectified Linear Unit (PReLU) Function | ✅ |
Exponential Linear Unit (ELU) Function | ✅ |
Scaled Exponential Linear Unit (SELU) Function | ✅ |
Swish Function | ✅ |
Gaussian Error Linear Unit (GELU) Function | ✅ |
Softmax Function | ✅ |
Gated Linear Unit (GLU) Function | ✅ |
ReGLU (ReLU + GLU) Function | ✅ |
GeGLU (GELU + GLU) Function | ✅ |
SwiGLU (Swish + GLU) Function | ✅ |
- ✅ linear_algebra_study_notes.ipynb - For Quick Revision
Topic | Status |
---|---|
Basics of Vector and real coordinate spaces | ✅ |
Addition and subtraction of vectors | ✅ |
Parameterizing lines in any dimensions | ✅ |
Linear combination of vectors and span | ✅ |
Linear dependence and independence | ✅ |
Linear subspaces and basis | ✅ |
Dot product of vectors | ✅ |
Cauchy-Schwarz and Vector Triangle Inequality | ✅ |
Angle between two vectors and orthogonal vectors | ✅ |
Cross product of vectors | ✅ |
Vector projections | ✅ |
Reduced Row Echelon Form (RREF) | ✅ |
System of Linear Equations | ✅ |
Matrix Multiplication, Null Space and Column Space | ✅ |
- ✅ fast_fourier_transform.ipynb - Performing Fourier Transform in O(nlogn) Time Complexity
Topic | Status |
---|---|
Quick intro to Fourier Transform | ✅ |
Fourier Transform using Discrete Fourier Transform (DFT) vs Fast Fourier Transform (FFT) | ███░░ |
Implementing Fast Fourier Transform | ✅ |
- ✅ similarity_search.ipynb - A Collection of Similarity Search Algorithms
Topic | Status |
---|---|
Jaccard Similarity | ✅ |
W-shingling | ✅ |
Levenshtein distance | ✅ |
TF-IDF | ✅ |
Best Matching 25 (BM25) | ✅ |
FAISS (IndexFlatL2) | ✅ |
FAISS (IndexFlatL2 with IndexIVFFlat) | ✅ |
FAISS (IndexFlatL2 with IndexIVFPQ) | ✅ |
FAISS (IndexFlatIP) | ✅ |
FAISS (IndexLSH) | ✅ |
FAISS (IndexHNSWFlat) | ✅ |
- ✅ calculus_study_notes.ipynb - For Quick Revision
Topic | Status |
---|---|
Limit | ✅ |
Squeeze Theorem | ✅ |
- ✅ complex_numbers.ipynb - Complex Numbers in Python
Topic | Status |
---|---|
Introduction | ✅ |
Addition | ✅ |
Subtraction | ✅ |
Multiplication | ✅ |
Division | ✅ |
Polar Representation | ✅ |