Artificial Intelligence

A comprehensive collection of resources, assignments, and implementations focused on the study of Artificial Intelligence. This project covers everything from basic simulations to advanced deep learning architectures using industry-standard frameworks.

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📂 Repository Structure

artificial-intelligence/
├── Notebooks/                        # Core technical implementations
│   ├── Basic/                        # Introductory AI concepts and foundational implementations
│   ├── ANN/                          # Artificial Neural Network architectures
│   └── CNN/                          # Convolutional Neural Networks (TensorFlow & Keras)
│       └── VGG19                     # Transfer learning with VGG19 pretrained model
├── document/                         # Academic and project-related documentation
│   └── AI_assignment_1_simple_simulation/
│       ├── notebook                  # Assignment in Jupyter Notebook format
│       └── report (PDF)              # Accompanying PDF report
└── LICENSE                           # Apache License 2.0

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🧠 Modules Overview

1. Notebooks/

The core of the repository — hands-on Jupyter Notebook implementations organized by topic:

• Basic — Introductory implementations covering foundational AI concepts and simple simulations.
• ANN (Artificial Neural Networks) — Exploration of neural network architectures, including design, training, and evaluation.
• CNN (Convolutional Neural Networks) — Deep learning models built with TensorFlow and Keras, featuring:
  • Custom CNN architectures for image-based tasks.
  • VGG19 as a pretrained model for transfer learning.

2. document/

Academic and project documentation: cover Fedarated Learning | Adaptive Feature Fusion | Octree Segmentation | DWT | GAT

• AI_assignment_1_simple_simulation — Available in both Jupyter Notebook and PDF formats, covering an introductory AI simulation task.

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Key Technologies

Technology	Role
Python	Primary programming language
Jupyter Notebook	Interactive development environment
TensorFlow	Deep learning framework
Keras	High-level neural network API
VGG19	Pretrained CNN for transfer learning

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Getting Started

Prerequisites

Ensure you have the following installed:

• Python 3.x
• Jupyter Notebook or JupyterLab
• TensorFlow (which includes Keras)

Installation

1. Clone the repository:

   git clone https://github.com/hasan2733/artificial-intelligence.git

2. Navigate to the project folder:

   cd artificial-intelligence

3. Install dependencies:

   pip install tensorflow jupyter numpy matplotlib

4. Launch Jupyter:

   jupyter notebook

5. Open any .ipynb file from the Notebooks/ directory to get started.

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⚖️ License

This project is licensed under the Apache License 2.0. See the LICENSE file for more information.

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👤 Author

Abid Hasan

• GitHub: @hasan2733