3D-Deep-Learning

3D Deep Learning Tutorials

3D Deep Learning Repository

Welcome to the 3D Deep Learning repository! This repository aims to provide a comprehensive set of tutorials on 3D deep learning using Python. Whether you're a beginner or an experienced practitioner, this resource will guide you through the fundamentals and advanced concepts of 3D deep learning.

Table of Contents

1. Introduction to 3D Deep Learning
2. Getting Started
3. Installation
4. Tutorials
   • Tutorial 1: Understanding 3D Data
   • Tutorial 2: Preprocessing 3D Data
   • Tutorial 3: Building 3D Convolutional Neural Networks
   • Tutorial 4: Transfer Learning for 3D Deep Learning
   • Tutorial 5: Evaluating 3D Deep Learning Models
   • Tutorial 6: Deploying 3D Models in Applications
5. Examples
6. Contributing
7. License

Introduction

Deep learning in 3D space has gained significant traction in various fields, including geospatial mapping, medical imaging, computer vision, robotics, autonomous driving, and more. This repository serves as a code learning hub for understanding and implementing 3D deep learning techniques using Python.

Getting Started

Before diving into the tutorials, make sure you have the necessary tools and libraries installed. Please refer to the Installation section for detailed instructions.

Installation

To get started with 3D Deep Learning, you'll need to set up your environment. Each code package is grounded with an how-to guide accessible on my Medium Page. You then have a section dedicated to the local setup. It usually involves this:

# Clone the repository
git clone https://github.com/username/3d-deep-learning.git

# Navigate to the project directory
cd 3d-deep-learning

# Install miniconda with Python version 3.10

# Create a virtual environment (optional but recommended)
conda create -n DEEPTUTO python=3.10

# Activate the virtual environment
conda acti

# Install dependencies using requirements (if set-up)
pip install -r requirements.txt

#Install dependencies using the given libraries in the Medium Article
pip install numpy matplotlib laspy keras

Tutorials

Tutorial 1: Understanding Artificial Neural Networks

In this tutorial, we cover the basics of working with Artificial Neural Networks to pursue our quest toward 3D Deep Learning

For starting the tutorial, please refer to the tutorials directory, and chose the relevant one

Tutorial 1: Understanding 3D Data

In this tutorial, we'll cover the basics of working with 3D data, including formats, visualization, and common preprocessing techniques.

Coming soon.

Tutorial 2: Preprocessing 3D Data

Learn about essential preprocessing steps for preparing 3D data for deep learning models. This includes data augmentation, normalization, and more.

Coming soon.

Tutorial 3: Building 3D Convolutional Neural Networks

Discover how to construct 3D CNN architectures for tasks such as classification, segmentation, and detection.

Coming soon.

Tutorial 4: Transfer Learning for 3D Deep Learning

Explore techniques to leverage pre-trained 3D models and adapt them for your specific tasks.

Coming soon.

Tutorial 5: Evaluating 3D Deep Learning Models

Learn how to assess the performance of your 3D deep learning models using various metrics and visualization tools.

Coming soon.

Tutorial 6: Deploying 3D Models in Applications

Understand the process of deploying 3D deep learning models in real-world applications, including considerations for hardware and software requirements.

Coming soon.

Contributing

I welcome contributions! If you have an idea for a new tutorial or want to improve existing content, please refer to the contributing guidelines.

License

This repository is licensed under the MIT License.

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Feel free to reach out with any questions, feedback, or suggestions. Happy learning! 🚀