# syedanazish-debug/lab-ai-ml architecture

> Architecture diagram of the GitHub repository syedanazish-debug/lab-ai-ml, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-29). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/syedanazish-debug/lab-ai-ml
- Repository: https://github.com/syedanazish-debug/lab-ai-ml

## Overview

This repository is a set of academic Python practicals, not one integrated application. The main workflow is a learner selecting and running a subject practical to observe printed results or visualizations. The graph preserves the documented NLP, neural-network, and image-processing topics; source excerpts are available for only some programs, so links between individual practicals are omitted where execution or data flow is not evidenced.

Main entry points: [`DIP`](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/DIP) (Run DIP practical), [`NN_PRACTICLE`](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NN_PRACTICLE) (Run NN practical), [`NLP_PRACTICLE`](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NLP_PRACTICLE) (Run NLP practical), [`NLP_PRACTICLE/4th_par.py`](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/4th_par.py) (Sentence segmentation)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_nlp["NLP practicals"]
  node_nlp_run["Run NLP practical"]
  node_nlp_sentence["Sentence segmentation<br/>[4th_par.py]"]
  node_nlp_brown["Brown corpus analysis<br/>[2nd_par.py]"]
  node_nlp_treebank["Treebank tagging<br/>[3rd_par.py]"]
  node_nlp_results["Text results"]
end

subgraph group_nn["Neural networks"]
  node_nn_run["Run NN practical"]
  node_nn_neuron["Neuron models<br/>[p1_mp_neuron.py]"]
  node_nn_perceptron["Perceptron learning"]
  node_nn_competitive["Winner-take-all<br/>[p7_winner_comp.py]"]
  node_nn_convergence["Convergence practical"]
  node_nn_backprop["Backpropagation"]
  node_nn_adaptive["Adaptive filtering<br/>[P10_.py]"]
  node_nn_convolution["Image convolution<br/>[p12_.py]"]
  node_nn_results["Predictions and plots"]
end

subgraph group_dip["Image processing"]
  node_dip_run["Run DIP practical"]
  node_dip_color["Color components<br/>[p1_color_m.py]"]
  node_dip_logic["Image logic operations<br/>[p2_logic_opt.py]"]
  node_dip_gray["Gray-level processing<br/>[p3_graylevel.py]"]
  node_dip_hist["Histogram processing<br/>[p4_hist.py]"]
  node_dip_dft["Discrete Fourier transform<br/>[p5_DFT.py]"]
  node_dip_convolution["Video convolution<br/>[p6_v_com.py]"]
  node_dip_rle["Run-length encoding<br/>[p7_RLE.py]"]
  node_dip_noise["Noise practical<br/>[p8_GN.py]"]
  node_dip_results["Image results"]
end

node_learner(("Learner"))

node_learner -->|"selects and runs"| node_nlp_run
node_nlp_run -->|"runs"| node_nlp_sentence
node_nlp_run -->|"runs"| node_nlp_brown
node_nlp_run -->|"runs"| node_nlp_treebank
node_nlp_sentence -->|"prints segments"| node_nlp_results
node_nlp_brown -->|"prints statistics"| node_nlp_results
node_nlp_treebank -->|"prints corpus data"| node_nlp_results
node_learner -->|"selects and runs"| node_nn_run
node_nn_run -->|"runs"| node_nn_neuron
node_nn_run -->|"runs"| node_nn_perceptron
node_nn_run -->|"runs"| node_nn_competitive
node_nn_run -->|"runs"| node_nn_convergence
node_nn_run -->|"runs"| node_nn_backprop
node_nn_run -->|"runs"| node_nn_adaptive
node_nn_run -->|"runs"| node_nn_convolution
node_nn_backprop -->|"prints predictions"| node_nn_results
node_nn_adaptive -->|"plots filter output"| node_nn_results
node_nn_convolution -->|"displays filtered images"| node_nn_results
node_learner -->|"selects and runs"| node_dip_run
node_dip_run -->|"runs"| node_dip_color
node_dip_run -->|"runs"| node_dip_logic
node_dip_run -->|"runs"| node_dip_gray
node_dip_run -->|"runs"| node_dip_hist
node_dip_run -->|"runs"| node_dip_dft
node_dip_run -->|"runs"| node_dip_convolution
node_dip_run -->|"runs"| node_dip_rle
node_dip_run -->|"runs"| node_dip_noise
node_dip_color -->|"displays components"| node_dip_results
node_dip_logic -->|"displays operations"| node_dip_results
node_dip_hist -->|"plots histograms"| node_dip_results

click node_nlp_run "https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NLP_PRACTICLE"
click node_nlp_sentence "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/4th_par.py"
click node_nlp_brown "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/2nd_par.py"
click node_nlp_treebank "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/3rd_par.py"
click node_nn_run "https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NN_PRACTICLE"
click node_nn_neuron "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p1_mp_neuron.py"
click node_nn_perceptron "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p4_sl_perceptron.py"
click node_nn_competitive "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p7_winner_comp.py"
click node_nn_convergence "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p8_convergence_th.py"
click node_nn_backprop "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p9_bp_ml_peerceptron.py"
click node_nn_adaptive "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/P10_.py"
click node_nn_convolution "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p12_.py"
click node_dip_run "https://github.com/syedanazish-debug/lab-ai-ml/tree/main/DIP"
click node_dip_color "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p1_color_m.py"
click node_dip_logic "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p2_logic_opt.py"
click node_dip_gray "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p3_graylevel.py"
click node_dip_hist "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p4_hist.py"
click node_dip_dft "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p5_DFT.py"
click node_dip_convolution "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p6_v_com.py"
click node_dip_rle "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p7_RLE.py"
click node_dip_noise "https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p8_GN.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_nlp_run,node_nlp_sentence,node_nlp_brown,node_nlp_treebank,node_nlp_results toneBlue
class node_nn_run,node_nn_neuron,node_nn_perceptron,node_nn_competitive,node_nn_convergence,node_nn_backprop,node_nn_adaptive,node_nn_convolution,node_nn_results toneAmber
class node_dip_run,node_dip_color,node_dip_logic,node_dip_gray,node_dip_hist,node_dip_dft,node_dip_convolution,node_dip_rle,node_dip_noise,node_dip_results toneMint
class node_learner toneIndigo
```

## Components

### NLP practicals

- [Run NLP practical](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NLP_PRACTICLE): `NLP_PRACTICLE`
- [Sentence segmentation](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/4th_par.py): `NLP_PRACTICLE/4th_par.py`
- [Brown corpus analysis](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/2nd_par.py): `NLP_PRACTICLE/2nd_par.py`
- [Treebank tagging](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NLP_PRACTICLE/3rd_par.py): `NLP_PRACTICLE/3rd_par.py`
- **Text results**

### Neural networks

- [Run NN practical](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/NN_PRACTICLE): `NN_PRACTICLE`
- [Neuron models](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p1_mp_neuron.py): `NN_PRACTICLE/p1_mp_neuron.py`
- [Perceptron learning](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p4_sl_perceptron.py): `NN_PRACTICLE/p4_sl_perceptron.py`
- [Winner-take-all](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p7_winner_comp.py): `NN_PRACTICLE/p7_winner_comp.py`
- [Convergence practical](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p8_convergence_th.py): `NN_PRACTICLE/p8_convergence_th.py`
- [Backpropagation](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p9_bp_ml_peerceptron.py): `NN_PRACTICLE/p9_bp_ml_peerceptron.py`
- [Adaptive filtering](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/P10_.py): `NN_PRACTICLE/P10_.py`
- [Image convolution](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/NN_PRACTICLE/p12_.py): `NN_PRACTICLE/p12_.py`
- **Predictions and plots**

### Image processing

- [Run DIP practical](https://github.com/syedanazish-debug/lab-ai-ml/tree/main/DIP): `DIP`
- [Color components](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p1_color_m.py): `DIP/p1_color_m.py`
- [Image logic operations](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p2_logic_opt.py): `DIP/p2_logic_opt.py`
- [Gray-level processing](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p3_graylevel.py): `DIP/p3_graylevel.py`
- [Histogram processing](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p4_hist.py): `DIP/p4_hist.py`
- [Discrete Fourier transform](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p5_DFT.py): `DIP/p5_DFT.py`
- [Video convolution](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p6_v_com.py): `DIP/p6_v_com.py`
- [Run-length encoding](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p7_RLE.py): `DIP/p7_RLE.py`
- [Noise practical](https://github.com/syedanazish-debug/lab-ai-ml/blob/main/DIP/p8_GN.py): `DIP/p8_GN.py`
- **Image results**

### Other components

- **Learner**

## Connections

- Learner → Run NLP practical: selects and runs
- Run NLP practical → Sentence segmentation: runs
- Run NLP practical → Brown corpus analysis: runs
- Run NLP practical → Treebank tagging: runs
- Sentence segmentation → Text results: prints segments
- Brown corpus analysis → Text results: prints statistics
- Treebank tagging → Text results: prints corpus data
- Learner → Run NN practical: selects and runs
- Run NN practical → Neuron models: runs
- Run NN practical → Perceptron learning: runs
- Run NN practical → Winner-take-all: runs
- Run NN practical → Convergence practical: runs
- Run NN practical → Backpropagation: runs
- Run NN practical → Adaptive filtering: runs
- Run NN practical → Image convolution: runs
- Backpropagation → Predictions and plots: prints predictions
- Adaptive filtering → Predictions and plots: plots filter output
- Image convolution → Predictions and plots: displays filtered images
- Learner → Run DIP practical: selects and runs
- Run DIP practical → Color components: runs
- Run DIP practical → Image logic operations: runs
- Run DIP practical → Gray-level processing: runs
- Run DIP practical → Histogram processing: runs
- Run DIP practical → Discrete Fourier transform: runs
- Run DIP practical → Video convolution: runs
- Run DIP practical → Run-length encoding: runs
- Run DIP practical → Noise practical: runs
- Color components → Image results: displays components
- Image logic operations → Image results: displays operations
- Histogram processing → Image results: plots histograms