🔭 Deep-Learning-Practice

A hands-on deep learning playground: curated FreeCodeCamp certification challenges, algorithm implementations, and structured I/O workflows.

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📖 Table of Contents

• ℹ️ About
• ✨ Features
• 📁 Repository Structure
• 🚀 Getting Started
• ⚙️ Usage
• 🤝 Contributing
• 📄 License

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ℹ️ About

Deep-Learning-Practice is a comprehensive collection of tutorials, code samples, and challenge solutions designed to reinforce both theory and practice in modern deep learning. Organized around the FreeCodeCamp (FCC) curriculum, this repository helps you prepare for the FCC Machine Learning certification while exploring core neural-network architectures and data pipelines.

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✨ Features

• 🎓 FCC Documentation & Notebooks
  Step-by-step guides and Jupyter notebooks aligned with FreeCodeCamp’s deep learning modules.
• 🏆 ML Challenge Solutions
  My solutions to FCC’s machine-learning tasks—ideal for certification prep and reference.
• 🧠 General Deep Learning
  Implementations of core algorithms and experiments with TensorFlow, PyTorch, Keras, and more.
• 🚀 Structured I/O Workflows
  Organized Inputs/ and Outputs/ folders for reproducible experiments and data management.

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📁 Repository Structure

Deep-Learning-Practice/
├── Documentations (By FCC)/                          # FCC deep learning reference material
├── Jupyter Files (By FCC)/                           # FCC notebooks & exercises
├── FreeCodeCamp ML Challenges/                       # My challenge solutions for FCC certification
├── General Deep Learning (Algorithms & Libraries)    # Custom implementations & library demos
├── Inputs/                                           # Datasets, feature files & raw inputs
├── Outputs/                                          # Trained models, logs & result files
└── README.md                                         # Project overview

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🚀 Getting Started

1. Clone the repository

   git clone https://github.com/Kratugautam99/Deep-Learning-Practice.git
   cd Deep-Learning-Practice

2. Set up a Python environment

   python3 -m venv .venv
   source .venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

   Typical packages include TensorFlow, PyTorch, scikit-learn, pandas, NumPy, matplotlib, and Jupyter.

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⚙️ Usage

• Explore FCC Notebooks

  jupyter notebook "Jupyter Files (By FCC)/{DLModel.ipynb filename}"

• Run Challenge Solutions

  python "FreeCodeCamp ML Challenges/{Challenge Solution}"

• Train a Model

  python "General Deep Learning (Algorithms & Libraries)/{Mentioned Files}" 

• FineTuning LLM

  python "LLM FineTuning/{Mentioned Files}"

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🤝 Contributing

Contributions, issue reports, and pull requests are welcome! To contribute:

1. Fork the repo
2. Create a branch:

   git checkout -b feature/YourFeature

3. Commit your changes:

   git commit -m "Add feature: description"

4. Push and open a Pull Request

Please follow the Code of Conduct.

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📄 License

This project is licensed under the MIT License.