# saransh-basu-01/ai_engineering architecture

> Architecture diagram of the GitHub repository saransh-basu-01/ai_engineering, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/saransh-basu-01/ai_engineering
- Repository: https://github.com/saransh-basu-01/ai_engineering

## Overview

This repository is a self-guided AI-engineering learning workspace, not a single runtime application. A learner explores topic notebooks and exercises, then runs from-scratch implementations and observes worked examples or experiment output. The tree and sampled Python sources support mathematical foundations, probability and inference, dimensionality reduction, and autodifferentiation/optimization; broader README capabilities such as data-processing pipelines are not represented by a dedicated subsystem in the supplied tree. Notebook internals were not sampled, so their wiring is not inferred.

Main entry points: [`Math_Fundamentals/Probability_Distributions/probability.py`](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Probability_Distributions/probability.py) (Probability code), [`Math_Fundamentals/Bayes_Rule/Bayes.py`](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Bayes_Rule/Bayes.py) (Bayes and classifier), [`Math_Fundamentals/Information_theory/information_entropy.py`](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Information_theory/information_entropy.py) (Entropy code), [`Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation/Autograd.py`](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation/Autograd.py) (Autograd and MLP)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_foundations["Math foundations"]
  node_linear["Linear algebra"]
  node_vectors["Vector operations"]
  node_transformations["Matrix transforms"]
  node_derivatives["Derivatives"]
end

subgraph group_probability["Probability and inference"]
  node_information["Information theory"]
  node_distributions["Probability distributions"]
  node_bayes["Bayes rule"]
  node_probability_impl["Probability code<br/>[probability.py]"]
  node_bayes_impl["Bayes and classifier<br/>[Bayes.py]"]
  node_entropy_impl["Entropy code"]
end

subgraph group_learning["Learning mechanics"]
  node_autodiff["Automatic differentiation"]
  node_autograd_impl["Autograd and MLP<br/>[Autograd.py]"]
  node_optimization["Gradient optimization"]
  node_optimizer_impl["Optimizers<br/>[optimizers.py]"]
end

subgraph group_representations["Data representations"]
  node_dimensionality["Dimensionality reduction"]
  node_pca_impl["PCA and comparisons<br/>[dim_reduction.py]"]
end

node_learner(("Learner"))
node_results(("Worked output"))

node_learner -->|"studies"| node_linear
node_learner -->|"studies"| node_vectors
node_learner -->|"studies"| node_transformations
node_learner -->|"studies"| node_derivatives
node_learner -->|"studies"| node_information
node_learner -->|"studies"| node_distributions
node_learner -->|"studies"| node_bayes
node_learner -->|"studies"| node_autodiff
node_learner -->|"studies"| node_optimization
node_learner -->|"studies"| node_dimensionality
node_learner -->|"runs examples"| node_probability_impl
node_learner -->|"runs examples"| node_bayes_impl
node_learner -->|"runs examples"| node_entropy_impl
node_learner -->|"runs examples"| node_autograd_impl
node_learner -->|"runs examples"| node_optimizer_impl
node_learner -->|"runs examples"| node_pca_impl
node_probability_impl -->|"prints examples"| node_results
node_bayes_impl -->|"prints examples"| node_results
node_entropy_impl -->|"prints examples"| node_results
node_autograd_impl -->|"prints examples"| node_results
node_optimizer_impl -->|"prints examples"| node_results
node_pca_impl -->|"prints examples"| node_results

click node_linear "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Linear_Algebra_intution"
click node_vectors "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Vector_matrices_and_operations"
click node_transformations "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Matrix_Transformations%20and%20EigenValues"
click node_derivatives "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Derivatives_and_Gradients"
click node_information "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Information_theory"
click node_distributions "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Probability_Distributions"
click node_bayes "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Bayes_Rule"
click node_probability_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Probability_Distributions/probability.py"
click node_bayes_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Bayes_Rule/Bayes.py"
click node_entropy_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Information_theory/information_entropy.py"
click node_autodiff "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation"
click node_autograd_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation/Autograd.py"
click node_optimization "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Optimization%3AGradient_Descent"
click node_optimizer_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Optimization%3AGradient_Descent/optimizers.py"
click node_dimensionality "https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Dimensionality_Reduction"
click node_pca_impl "https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Dimensionality_Reduction/dim_reduction.py"

classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a
classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f
classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a
class node_linear,node_vectors,node_transformations,node_derivatives toneBlue
class node_information,node_distributions,node_bayes,node_probability_impl,node_bayes_impl,node_entropy_impl toneAmber
class node_autodiff,node_autograd_impl,node_optimization,node_optimizer_impl toneMint
class node_dimensionality,node_pca_impl toneRose
class node_learner,node_results toneIndigo
```

## Components

### Math foundations

- [Linear algebra](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Linear_Algebra_intution): `Math_Fundamentals/Linear_Algebra_intution`
- [Vector operations](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Vector_matrices_and_operations): `Math_Fundamentals/Vector_matrices_and_operations`
- [Matrix transforms](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Matrix_Transformations%20and%20EigenValues): `Math_Fundamentals/Matrix_Transformations and EigenValues`
- [Derivatives](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Derivatives_and_Gradients): `Math_Fundamentals/Derivatives_and_Gradients`

### Probability and inference

- [Information theory](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Information_theory): `Math_Fundamentals/Information_theory`
- [Probability distributions](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Probability_Distributions): `Math_Fundamentals/Probability_Distributions`
- [Bayes rule](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Bayes_Rule): `Math_Fundamentals/Bayes_Rule`
- [Probability code](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Probability_Distributions/probability.py): `Math_Fundamentals/Probability_Distributions/probability.py`
- [Bayes and classifier](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Bayes_Rule/Bayes.py): `Math_Fundamentals/Bayes_Rule/Bayes.py`
- [Entropy code](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Information_theory/information_entropy.py): `Math_Fundamentals/Information_theory/information_entropy.py`

### Learning mechanics

- [Automatic differentiation](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation): `Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation`
- [Autograd and MLP](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation/Autograd.py): `Math_Fundamentals/Chain_Rule_And_Automatic_Differentiation/Autograd.py`
- [Gradient optimization](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Optimization%3AGradient_Descent): `Math_Fundamentals/Optimization:Gradient_Descent`
- [Optimizers](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Optimization%3AGradient_Descent/optimizers.py): `Math_Fundamentals/Optimization:Gradient_Descent/optimizers.py`

### Data representations

- [Dimensionality reduction](https://github.com/saransh-basu-01/ai_engineering/tree/main/Math_Fundamentals/Dimensionality_Reduction): `Math_Fundamentals/Dimensionality_Reduction`
- [PCA and comparisons](https://github.com/saransh-basu-01/ai_engineering/blob/main/Math_Fundamentals/Dimensionality_Reduction/dim_reduction.py): `Math_Fundamentals/Dimensionality_Reduction/dim_reduction.py`

### Other components

- **Learner**
- **Worked output**

## Connections

- Learner → Linear algebra: studies
- Learner → Vector operations: studies
- Learner → Matrix transforms: studies
- Learner → Derivatives: studies
- Learner → Information theory: studies
- Learner → Probability distributions: studies
- Learner → Bayes rule: studies
- Learner → Automatic differentiation: studies
- Learner → Gradient optimization: studies
- Learner → Dimensionality reduction: studies
- Learner → Probability code: runs examples
- Learner → Bayes and classifier: runs examples
- Learner → Entropy code: runs examples
- Learner → Autograd and MLP: runs examples
- Learner → Optimizers: runs examples
- Learner → PCA and comparisons: runs examples
- Probability code → Worked output: prints examples
- Bayes and classifier → Worked output: prints examples
- Entropy code → Worked output: prints examples
- Autograd and MLP → Worked output: prints examples
- Optimizers → Worked output: prints examples
- PCA and comparisons → Worked output: prints examples