A journey from basics to mastery in multimodal learning!
Welcome to a world where text, images, audio, and video come together to create intelligent systems. 🚀
Multimodal learning integrates multiple types of data modalities (e.g., text, image, audio, video) to enhance machine learning systems. It’s widely applied in fields like:
- Autonomous Driving: Combining camera, radar, and LIDAR data.
- Healthcare: Using MRI scans and patient records for diagnosis.
- Smart Assistants: Processing text and voice inputs for human-like interactions.
- E-commerce: Enriching product searches with text and image inputs.
| Section | Description |
|---|---|
| Introduction | Basics of multimodal learning and its importance. |
| Models | Dive into cutting-edge multimodal models like CLIP and ALIGN. |
| Datasets | Explore publicly available multimodal datasets. |
| Tutorials | Hands-on guides to build and experiment with multimodal systems. |
| Research | Stay updated with the latest trends and research directions. |
| Projects | Real-world applications and use cases. |
| Tools | Libraries and platforms to kickstart your journey. |
Hi there! I'm WangchukMind, a passionate Ph.D. student in Software Engineering and an AI enthusiast. 💡
I created this repository to share knowledge and help others explore the fascinating world of multimodal learning.
Feel free to connect, contribute, or just enjoy the content! 😊
- Python 3.8 or later
- PyTorch or TensorFlow
- Basic knowledge of deep learning
- Clone the repository:
git clone https://github.com/WangchukMind/Multimodal-Learning-101.git