# prince-chhirolya/projects-series architecture

> Architecture diagram of the GitHub repository prince-chhirolya/projects-series, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-05-13). It is an AI-made overview: check details against the source.

- Interactive diagram: https://gitdiagram.com/prince-chhirolya/projects-series
- Repository: https://github.com/prince-chhirolya/projects-series

## Overview

This repository is a portfolio-style monorepo of many **standalone ML/data-science projects**, not a single integrated application. The main architectural boundary is **folder-per-project**: each directory contains its own notebook, sample data, and sometimes a small app or model artifact.

1. **Top-level shape**
   - The root contains only repository hygiene files plus dozens of independent project folders.
   - There is no shared backend, central package, or common runtime across all projects.
   - `.github/dependabot.yml` suggests dependency maintenance is handled at the repo level, but the projects themselves are isolated.

2. **Common project pattern**
   - Most folders follow the same structure: `README.md` + one or more notebooks (`.ipynb`) + local datasets (`.csv`, images, JSON, videos) + occasional trained outputs.
   - This means the primary data flow is usually: **local dataset → notebook/script → trained model / analysis output → optional demo UI**.

3. **Notebook-centric analysis and experimentation**
   - Many projects are pure notebooks for exploratory analysis, model training, and evaluation.
   - Examples include statsmodels analysis, causal inference, epidemic modeling, Lotka-Volterra simulations, image segmentation, GAN augmentation, and several classification notebooks.
   - These are typically self-contained and use the notebook as both implementation and documentation layer.

4. **Classical ML / tabular prediction track**
   - A large cluster focuses on tabular ML with CSV inputs and standard supervised learning workflows.
   - Examples: diabetes prediction, traffic volume prediction, cancer prediction, frog toxicity prediction, used-car price prediction, fake news detection, writer fingerprinting, Naive Bayes classification.
   - The architectural pattern is: **CSV data → preprocessing → model training/evaluation → saved model or notebook output**.

5. **Deep learning / vision track**
   - Another cluster uses CNNs, ViT, YOLO, OpenCV, and related computer vision tooling.
   - Examples: road sign recognition, cats vs dogs classification, anomaly detection in medical images, object detection web app, safety helmet detection, hand sign detection, multiple hand detection, image forgery detection, vision transformer image classification.
   - These projects often include heavier artifacts such as pretrained weights (`yolov8n.pt`), trained model folders, or dataset builders.

6. **Interactive web app layer**
   - Some projects add a small UI layer on top of model or data logic.
   - Streamlit appears in multiple places: the data-centric web app, the YOLOv8 object detection app, and the population modeling web-sharing examples.
   - One project uses a Flask-style app structure for image forgery detection (`app.py`, `templates`, `static`, `uploads`), while another uses Gradio for image colorization.
   - In these cases the flow is: **user input → model/analysis logic → rendered result in web UI**.

7. **Data visualization and geospatial reporting**
   - Several projects are centered on visual analytics rather than prediction.
   - Examples: Folium crash maps, Plotly/Mapbox geospatial visualization, Streamlit-based map/plot explorer, stats visualizations in notebooks.
   - These projects typically transform CSV data into charts or maps, with no persistent service layer.

8. **NLP / text / audio projects**
   - There is a distinct set of language and speech tasks: chatbot with NLTK/TensorFlow, email spam classification with LSTM and Word2Vec, auto-tagging content categorization, fake news detection, knowledge graph extraction, and Whisper speech recognition fine-tuning.
   - The data flow here is mostly **text corpus or intents JSON → preprocessing/tokenization/embeddings → model or extracted structure**.

9. **Simulation and scientific modeling**
   - The population modeling folder contains multiple subdomains: epidemic SIR variants, Game of Life, and Lotka-Volterra dynamics.
   - These are simulation-heavy and often include interactive notebooks, plots, phase-space analysis, and parameter fitting.
   - The “Web_Sharing” subfolder shows these simulations can be exported to lightweight HTML or Streamlit/Datapane views.

10. **Artifacts and pretrained outputs**
   - Some folders include saved assets that are part of the project boundary: `model.json`, `trained_model/`, `model.onnx`, `yolov8n.pt`, dataset metadata, HTML examples, and generated media files.
   - These artifacts are not shared globally; they belong to the specific project that produced them.

11. **Storage and I/O model**
   - The repo is file-based and local-first.
   - Inputs are usually bundled alongside code as CSVs, images, videos, or JSON.
   - Outputs are likewise local: notebooks, exported HTML, trained weights, or app uploads folders.
   - There is no indication of a central database, message bus, or cloud service dependency.

12. **Architecture takeaway for a diagram**
   - The most accurate diagram is not a single system diagram, but a **hub-and-spoke collection of independent mini-systems**.
   - At the top level: “Personal Projects Series” as the repository container.
   - Below that: several parallel lanes such as **tabular ML**, **computer vision**, **NLP/audio**, **simulation**, and **interactive web demos**.
   - Each lane typically connects **data files → notebook/script → optional model artifact/UI**, with minimal coupling across folders.

Main entry points: [`Population Modeling and Simulations in Python`](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python) (Population), [`Build an Object Detection Web App with YOLOv8 and Streamlit`](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Build%20an%20Object%20Detection%20Web%20App%20with%20YOLOv8%20and%20Streamlit) (YOLO App), [`Image Forgery Detector/ImageForgeApp`](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Image%20Forgery%20Detector/ImageForgeApp) (Forgery App), [`Population Modeling and Simulations in Python/Web_Sharing`](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python/Web_Sharing) (Web Sharing)

## Diagram (Mermaid)

```mermaid
flowchart TD

subgraph group_repo["Repository"]
  node_repo_root["projects-series<br/>monorepo"]
  node_lane_tabular(("Tabular ML<br/>lane"))
  node_lane_vision(("Vision<br/>lane"))
  node_lane_nlp_audio(("NLP & Audio<br/>lane"))
  node_lane_simulation(("Simulation<br/>lane"))
  node_lane_viz(("Visualization<br/>lane"))
  node_lane_apps(("Interactive Apps<br/>lane"))
end

subgraph group_tabular["Tabular ML"]
  node_statsmodels["Statsmodels<br/>analysis notebook"]
  node_diabetes["Diabetes Model<br/>keras notebook"]
  node_traffic["Traffic Volume<br/>ml notebook"]
  node_frog_toxicity["Frog Toxicity<br/>xgboost notebook"]
  node_writer_fingerprint["Writer IDs<br/>ml notebook"]
  node_fake_news["Fake News<br/>sklearn notebook"]
end

subgraph group_vision["Computer Vision"]
  node_road_signs["Road Signs<br/>vision notebook"]
  node_cats_dogs["Cats vs Dogs<br/>cnn notebook"]
  node_anomaly_img["Image Anomaly<br/>vision pipeline"]
  node_object_detect["YOLO App<br/>detection app"]
  node_helmet["Helmet Detect<br/>yolo notebook"]
  node_forgery_app["Forgery App<br/>flask app"]
  node_deoldify["Colorize<br/>gradio notebook"]
end

subgraph group_nlp_audio["NLP & Audio"]
  node_chatbot["Chatbot<br/>nlp notebook"]
  node_email_lstm["Email Spam<br/>lstm notebook"]
  node_auto_tag["Auto-Tagging<br/>text notebook"]
  node_whisper["Whisper<br/>speech notebook"]
  node_music_ai["Music Gen<br/>audio notebook"]
  node_knowledge_graph["Knowledge Graph<br/>nlp notebook"]
end

subgraph group_simulation["Simulation"]
  node_pop_modeling["Population<br/>simulation suite"]
  node_epidemic["Epidemics<br/>sir notebooks"]
  node_life["Game of Life<br/>simulation notebooks"]
  node_lotka["Lotka-Volterra<br/>dynamics notebooks"]
end

subgraph group_viz["Visualization"]
  node_geo_folium["Crash Maps<br/>geospatial notebook"]
  node_geo_plotly["Map Visuals<br/>plotly notebook"]
end

subgraph group_apps["Interactive Apps"]
  node_web_share["Web Sharing<br/>share layer"]
  node_data_app["Data App<br/>streamlit app"]
end

node_repo_root -->|"contains"| node_lane_tabular
node_repo_root -->|"contains"| node_lane_vision
node_repo_root -->|"contains"| node_lane_nlp_audio
node_repo_root -->|"contains"| node_lane_simulation
node_repo_root -->|"contains"| node_lane_viz
node_repo_root -->|"contains"| node_lane_apps
node_lane_tabular -->|"example"| node_statsmodels
node_lane_tabular -->|"example"| node_diabetes
node_lane_tabular -->|"example"| node_traffic
node_lane_tabular -->|"example"| node_frog_toxicity
node_lane_tabular -->|"example"| node_writer_fingerprint
node_lane_tabular -->|"example"| node_fake_news
node_lane_vision -->|"example"| node_road_signs
node_lane_vision -->|"example"| node_cats_dogs
node_lane_vision -->|"example"| node_anomaly_img
node_lane_vision -->|"example"| node_object_detect
node_lane_vision -->|"example"| node_helmet
node_lane_vision -->|"example"| node_forgery_app
node_lane_vision -->|"example"| node_deoldify
node_lane_nlp_audio -->|"example"| node_chatbot
node_lane_nlp_audio -->|"example"| node_email_lstm
node_lane_nlp_audio -->|"example"| node_auto_tag
node_lane_nlp_audio -->|"example"| node_whisper
node_lane_nlp_audio -->|"example"| node_music_ai
node_lane_nlp_audio -->|"example"| node_knowledge_graph
node_lane_simulation -->|"umbrella"| node_pop_modeling
node_pop_modeling -->|"contains"| node_epidemic
node_pop_modeling -->|"contains"| node_life
node_pop_modeling -->|"contains"| node_lotka
node_pop_modeling -->|"exports"| node_web_share
node_lane_viz -->|"example"| node_geo_folium
node_lane_viz -->|"example"| node_geo_plotly
node_lane_apps -->|"example"| node_data_app
node_lane_apps -.->|"shares pattern"| node_object_detect
node_lane_apps -.->|"shares pattern"| node_forgery_app
node_lane_apps -.->|"shares pattern"| node_web_share

click node_statsmodels "https://github.com/prince-chhirolya/projects-series/tree/main/Analyze Data with statsmodels"
click node_diabetes "https://github.com/prince-chhirolya/projects-series/tree/main/Diabetes Prediction Using Keras"
click node_traffic "https://github.com/prince-chhirolya/projects-series/tree/main/Predict Traffic Volume Using Machine Learning"
click node_frog_toxicity "https://github.com/prince-chhirolya/projects-series/tree/main/Predict Frog Toxicity with Python and XGBoost"
click node_writer_fingerprint "https://github.com/prince-chhirolya/projects-series/tree/main/Detect a Writer’s Fingerprints Using Machine Learning"
click node_fake_news "https://github.com/prince-chhirolya/projects-series/tree/main/Fake News Detection Using Scikit-learn"
click node_road_signs "https://github.com/prince-chhirolya/projects-series/tree/main/Build a Road Sign Recognition System with CNN"
click node_cats_dogs "https://github.com/prince-chhirolya/projects-series/tree/main/Cats vs Dogs Classification with CNN"
click node_anomaly_img "https://github.com/prince-chhirolya/projects-series/tree/main/Anomaly Detection in Medical Images"
click node_object_detect "https://github.com/prince-chhirolya/projects-series/tree/main/Build an Object Detection Web App with YOLOv8 and Streamlit"
click node_helmet "https://github.com/prince-chhirolya/projects-series/tree/main/Building a System for Safety Helmet Detection Based on YOLOv5"
click node_forgery_app "https://github.com/prince-chhirolya/projects-series/tree/main/Image Forgery Detector/ImageForgeApp"
click node_deoldify "https://github.com/prince-chhirolya/projects-series/tree/main/Colorize Your Grayscale Images Using DeOldify and Gradio"
click node_chatbot "https://github.com/prince-chhirolya/projects-series/tree/main/Develop a Chatbot Using Python, NLTK, and TensorFlow"
click node_email_lstm "https://github.com/prince-chhirolya/projects-series/tree/main/Email Classification with LSTM and Word2Vec"
click node_auto_tag "https://github.com/prince-chhirolya/projects-series/tree/main/Auto-Tagging System for Content Categorization"
click node_whisper "https://github.com/prince-chhirolya/projects-series/tree/main/Speech Recognition with Whisper"
click node_music_ai "https://github.com/prince-chhirolya/projects-series/tree/main/Generating New Music with Artificial Intelligence"
click node_pop_modeling "https://github.com/prince-chhirolya/projects-series/tree/main/Population Modeling and Simulations in Python"
click node_epidemic "https://github.com/prince-chhirolya/projects-series/tree/main/Population Modeling and Simulations in Python/Epidemic_Modeling"
click node_life "https://github.com/prince-chhirolya/projects-series/tree/main/Population Modeling and Simulations in Python/Game_Of_Life"
click node_lotka "https://github.com/prince-chhirolya/projects-series/tree/main/Population Modeling and Simulations in Python/Lotka_Volterra"
click node_web_share "https://github.com/prince-chhirolya/projects-series/tree/main/Population Modeling and Simulations in Python/Web_Sharing"
click node_geo_folium "https://github.com/prince-chhirolya/projects-series/tree/main/Visualize Geospatial Data Using Folium"
click node_geo_plotly "https://github.com/prince-chhirolya/projects-series/tree/main/Visualize Geospatial Data Using Plotly and Mapbox"
click node_data_app "https://github.com/prince-chhirolya/projects-series/tree/main/Build a Data-centric Web App Using Streamlit"
click node_knowledge_graph "https://github.com/prince-chhirolya/projects-series/tree/main/Create a Knowledge Graph from Text"

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_repo_root,node_lane_tabular,node_lane_vision,node_lane_nlp_audio,node_lane_simulation,node_lane_viz,node_lane_apps,node_web_share,node_data_app toneBlue
class node_statsmodels,node_diabetes,node_traffic,node_frog_toxicity,node_writer_fingerprint,node_fake_news toneAmber
class node_road_signs,node_cats_dogs,node_anomaly_img,node_object_detect,node_helmet,node_forgery_app,node_deoldify toneMint
class node_chatbot,node_email_lstm,node_auto_tag,node_whisper,node_music_ai,node_knowledge_graph toneRose
class node_pop_modeling,node_epidemic,node_life,node_lotka toneIndigo
class node_geo_folium,node_geo_plotly toneTeal
```

## Components

### Repository

Monorepo container and hygiene files

- **projects-series**: monorepo · Portfolio repository of isolated ML/data-science projects
- **Tabular ML**: lane · Independent classical ML projects on local datasets
- **Vision**: lane · Image classification, detection, and segmentation projects
- **NLP & Audio**: lane · Text, chat, speech, and media generation projects
- **Simulation**: lane · Population dynamics and agent-based modeling
- **Visualization**: lane · Geospatial and chart-driven analysis projects
- **Interactive Apps**: lane · Small UI layers wrapped around notebooks or models

### Tabular ML

CSV-driven supervised learning and statistical analysis

- [Statsmodels](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Analyze%20Data%20with%20statsmodels): analysis notebook · Statistical analysis over housing data · `Analyze Data with statsmodels`
- [Diabetes Model](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Diabetes%20Prediction%20Using%20Keras): keras notebook · Binary prediction model with saved JSON artifact · `Diabetes Prediction Using Keras`
- [Traffic Volume](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Predict%20Traffic%20Volume%20Using%20Machine%20Learning): ml notebook · Supervised prediction on traffic data · `Predict Traffic Volume Using Machine Learning`
- [Frog Toxicity](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Predict%20Frog%20Toxicity%20with%20Python%20and%20XGBoost): xgboost notebook · Tabular toxicity prediction with ONNX model artifact · `Predict Frog Toxicity with Python and XGBoost`
- [Writer IDs](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Detect%20a%20Writer%E2%80%99s%20Fingerprints%20Using%20Machine%20Learning): ml notebook · Handwriting-based writer classification · `Detect a Writer’s Fingerprints Using Machine Learning`
- [Fake News](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Fake%20News%20Detection%20Using%20Scikit-learn): sklearn notebook · Text classification for misinformation detection · `Fake News Detection Using Scikit-learn`

### Computer Vision

CNN, ViT, YOLO, and image-processing projects

- [Road Signs](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Build%20a%20Road%20Sign%20Recognition%20System%20with%20CNN): vision notebook · CNN and ViT experiments for road sign recognition · `Build a Road Sign Recognition System with CNN`
- [Cats vs Dogs](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Cats%20vs%20Dogs%20Classification%20with%20CNN): cnn notebook · Image classification on a classic binary dataset · `Cats vs Dogs Classification with CNN`
- [Image Anomaly](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Anomaly%20Detection%20in%20Medical%20Images): vision pipeline · Medical image anomaly detection and dataset builder · `Anomaly Detection in Medical Images`
- [YOLO App](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Build%20an%20Object%20Detection%20Web%20App%20with%20YOLOv8%20and%20Streamlit): detection app · YOLOv8 inference wrapped in Streamlit · `Build an Object Detection Web App with YOLOv8 and Streamlit`
- [Helmet Detect](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Building%20a%20System%20for%20Safety%20Helmet%20Detection%20Based%20on%20YOLOv5): yolo notebook · Safety helmet detection workflow · `Building a System for Safety Helmet Detection Based on YOLOv5`
- [Forgery App](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Image%20Forgery%20Detector/ImageForgeApp): flask app · Image forgery detector with templates and uploads · `Image Forgery Detector/ImageForgeApp`
- [Colorize](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Colorize%20Your%20Grayscale%20Images%20Using%20DeOldify%20and%20Gradio): gradio notebook · Grayscale image colorization demo · `Colorize Your Grayscale Images Using DeOldify and Gradio`

### NLP & Audio

Text, language, speech, and sequence modeling

- [Chatbot](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Develop%20a%20Chatbot%20Using%20Python%2C%20NLTK%2C%20and%20TensorFlow): nlp notebook · Intent-based chatbot using NLTK and TensorFlow · `Develop a Chatbot Using Python, NLTK, and TensorFlow`
- [Email Spam](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Email%20Classification%20with%20LSTM%20and%20Word2Vec): lstm notebook · Spam filtering with embeddings and sequence modeling · `Email Classification with LSTM and Word2Vec`
- [Auto-Tagging](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Auto-Tagging%20System%20for%20Content%20Categorization): text notebook · Content categorization and tagging from text data · `Auto-Tagging System for Content Categorization`
- [Whisper](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Speech%20Recognition%20with%20Whisper): speech notebook · Speech recognition fine-tuning experiments · `Speech Recognition with Whisper`
- [Music Gen](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Generating%20New%20Music%20with%20Artificial%20Intelligence): audio notebook · Artificial music generation experiment · `Generating New Music with Artificial Intelligence`
- [Knowledge Graph](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Create%20a%20Knowledge%20Graph%20from%20Text): nlp notebook · Text-to-graph extraction with HTML example output · `Create a Knowledge Graph from Text`

### Simulation

Scientific modeling and dynamical systems

- [Population](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python): simulation suite · Scientific modeling umbrella for several dynamics notebooks · `Population Modeling and Simulations in Python`
- [Epidemics](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python/Epidemic_Modeling): sir notebooks · Deterministic, agent-based, and stochastic epidemic models · `Population Modeling and Simulations in Python/Epidemic_Modeling`
- [Game of Life](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python/Game_Of_Life): simulation notebooks · Cellular automaton simulation and animation · `Population Modeling and Simulations in Python/Game_Of_Life`
- [Lotka-Volterra](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python/Lotka_Volterra): dynamics notebooks · Predator-prey and growth dynamics with interactive analysis · `Population Modeling and Simulations in Python/Lotka_Volterra`

### Visualization

Maps, charts, and geospatial reporting

- [Crash Maps](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Visualize%20Geospatial%20Data%20Using%20Folium): geospatial notebook · Folium-based map visualization of crash records · `Visualize Geospatial Data Using Folium`
- [Map Visuals](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Visualize%20Geospatial%20Data%20Using%20Plotly%20and%20Mapbox): plotly notebook · Plotly and Mapbox geospatial visualization · `Visualize Geospatial Data Using Plotly and Mapbox`

### Interactive Apps

Streamlit, Flask, Gradio, and web demos

- [Web Sharing](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Population%20Modeling%20and%20Simulations%20in%20Python/Web_Sharing): share layer · HTML and Streamlit/Datapane exports for simulations · `Population Modeling and Simulations in Python/Web_Sharing`
- [Data App](https://github.com/prince-chhirolya/projects-series/tree/HEAD/Build%20a%20Data-centric%20Web%20App%20Using%20Streamlit): streamlit app · Data-centric explorer with local preprocessing and plotting helpers · `Build a Data-centric Web App Using Streamlit`

## Connections

- projects-series → Tabular ML: contains · Top-level lane in the portfolio repo
- projects-series → Vision: contains · Top-level lane in the portfolio repo
- projects-series → NLP & Audio: contains · Top-level lane in the portfolio repo
- projects-series → Simulation: contains · Top-level lane in the portfolio repo
- projects-series → Visualization: contains · Top-level lane in the portfolio repo
- projects-series → Interactive Apps: contains · Top-level lane in the portfolio repo
- Tabular ML → Statsmodels: example · Statistical analysis notebook over CSV data
- Tabular ML → Diabetes Model: example · Keras-based tabular prediction
- Tabular ML → Traffic Volume: example · Traffic volume prediction workflow
- Tabular ML → Frog Toxicity: example · XGBoost tabular model with exported ONNX artifact
- Tabular ML → Writer IDs: example · Writer classification from tabular features
- Tabular ML → Fake News: example · Classical text/tabular classifier
- Vision → Road Signs: example · CNN/ViT image classification
- Vision → Cats vs Dogs: example · Binary image classification
- Vision → Image Anomaly: example · Medical image anomaly detection workflow
- Vision → YOLO App: example · YOLOv8 detection app
- Vision → Helmet Detect: example · YOLO-based safety helmet detection
- Vision → Forgery App: example · Flask image-forgery demo
- Vision → Colorize: example · Gradio image colorization demo
- NLP & Audio → Chatbot: example · Intent chatbot notebook and script
- NLP & Audio → Email Spam: example · Sequence model for spam filtering
- NLP & Audio → Auto-Tagging: example · Content tagging and categorization
- NLP & Audio → Whisper: example · Speech recognition fine-tuning
- NLP & Audio → Music Gen: example · Music generation experiment
- NLP & Audio → Knowledge Graph: example · Text mining into graph structure
- Simulation → Population: umbrella · Parent folder for simulation subdomains
- Population → Epidemics: contains · Epidemic modeling notebooks
- Population → Game of Life: contains · Game of Life notebooks
- Population → Lotka-Volterra: contains · Lotka-Volterra and growth notebooks
- Population → Web Sharing: exports · HTML/Streamlit/Datapane sharing layer
- Visualization → Crash Maps: example · Folium map analysis
- Visualization → Map Visuals: example · Plotly/Mapbox geospatial visualization
- Interactive Apps → Data App: example · Streamlit data explorer with preprocessing helpers
- Interactive Apps → YOLO App: shares pattern · Small model-serving UI around YOLO inference
- Interactive Apps → Forgery App: shares pattern · Web UI wrapping image analysis logic
- Interactive Apps → Web Sharing: shares pattern · Lightweight publishing of notebook outputs