Deep Learning Research

ML From Scratch

Deconstructing intelligence by building it from the ground up using Pure NumPy.

Python 3.10

NumPy

Mathematics

The Problem

Black Box Dependency

Modern machine learning libraries like Scikit-Learn and TensorFlow are incredible feats of engineering, but they often abstract away the mathematical beauty of the algorithms.

Aspiring engineers often learn to just "import magic" without truly understanding the calculus and linear algebra happening under the hood. This shallow understanding limits the ability to debug complex models, optimize performance, or innovate beyond pre-built solutions.

The Solution

First Principles Approach

An educational research initiative to build every major classical machine learning algorithm completely from scratch using only Python and NumPy.

No automatic differentiation, no pre-built optimizers. Just raw gradients, matrix multiplications, and statistical theory translated directly into efficient, vectorized code.

How It Works

📐

First Principles

Explicit weight updates and gradient calculations.

∫

Mathematical Depth

Derivations accompanying every implementation.

⚡

Vectorization

Optimizing loops using NumPy broadcasting.

Implemented Algorithms

Linear Regression

Logistic Regression

K-Nearest Neighbors

Naive Bayes

Perceptron

Support Vector Machine

Decision Tree

Random Forest

Principal Component Analysis

K-Means Clustering

AdaBoost

Linear Discriminant Analysis

The Impact

✓
Mathematical IntimacyAchieved a deep, intuitive understanding of the calculus and statistics powering intelligence.
✓
Debugging SuperpowerDeveloped the ability to diagnose model issues (vanishing gradients, overfitting) by understanding their root mathematical causes.
✓
Optimization MindsetMastered vectorization techniques in NumPy, learning how to write high-performance Python code.

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MM.

ML From Scratch

The Problem

Black Box Dependency

The Solution

First Principles Approach

How It Works

First Principles

Mathematical Depth

Vectorization

Implemented Algorithms

The Impact

ML From Scratch

The Problem

Black Box Dependency

The Solution

First Principles Approach

How It Works

First Principles

Mathematical Depth

Vectorization

Implemented Algorithms

The Impact