This repository contains all code implementations and supplementary materials for the book Artificial Intelligence: Data Representation and Problem Solving. The book explores AI's role in data representation and problem-solving, detailing both theoretical aspects and practical applications.
AI is transforming industries and redefining problem-solving. This book serves as a practical guide, offering an intuitive understanding of AI’s architecture and applications. It emphasizes AI as a tool for human-machine collaboration, enhancing our ability to solve problems rather than replacing us.
The book explores AI’s strengths in Representation, Classification, Exploration, and Generation, demonstrating how these concepts apply to text, images, sound, and numbers. It balances theoretical insights with hands-on coding examples, making AI accessible to a broad audience.
Ultimately, the key message is that well-defined problems lead to better solutions. As AI takes on complex computational tasks, our role shifts toward defining problems effectively. By understanding AI’s strengths and fostering collaboration, we can leverage its potential to shape the future.
This repository provides hands-on code implementations for the book’s core concepts, including:
📦 ai-data-repr-problem-solving
├─📂 code # Code implementations from the book
│ ├─📂 1_Representation_Learning
│ │ ├─📂 1_sequence_to_sequence_learning
│ │ └─📂 2_data_embedding_to_numbers
│ ├─📂 2_Classification
│ │ ├─📂 1_head_based
│ │ └─📂 2_prompt_based
│ ├─📂 3_Generation
│ │ ├─📂 1_image
│ │ │ ├─📂 01_ae_family
│ │ │ └─📂 02_gan_family
│ │ ├─📂 2_text
│ │ └─📂 3_sound
│ └─📂 4_Exploration
├─📂 supplementary # Additional materials and resources
├─📜 README.md # Project overview and setup guide
└─📜 requirements.txt # List of dependencies (if applicable)
- Clone the repository:
git clone https://github.com/hugman/ai-data-repr-problem-solving.git
- Navigate to the project directory:
cd ai-data-repr-problem-solving - Install dependencies:
pip install -r requirements.txt
All code in this repository is implemented using Jupyter Notebook (.ipynb) format. Each notebook has been tested on Google Colab Free Tier to ensure smooth execution.
The recommended way to run these codes is using Google Colab, as it provides an easy-to-use environment with pre-installed dependencies and GPU support.
This project is licensed under the MIT License with an additional NOTICE file that prohibits commercial use.
- You are free to use, modify, and distribute the code for personal, academic, and research purposes.
- Commercial use (resale, proprietary inclusion, paid services, etc.) is strictly prohibited.
For more details, please refer to the LICENSE and NOTICE files.
This is a personal project, and external contributions are not accepted.
For any questions or inquiries, feel free to reach out!