Repo Description:

🤟 Sign Language Prediction with CNN 🤖

Welcome to my GitHub repository dedicated to a Deep Learning project focused on Sign Language Prediction using Convolutional Neural Networks (CNNs). This project revolves around building a robust multiclass classification model for interpreting sign language gestures.

🌐 Overview:

• Model Architecture: Implemented a CNN-based architecture for efficient feature extraction and classification.
• Dataset: Utilized the MNIST dataset specifically curated for sign language gestures.
• Classification: Employed a multiclass classification approach to accurately predict various sign language symbols.
• Techniques: Explored and implemented convolution and pooling layers to enhance the model's ability to recognize intricate patterns in sign language images.

🔍 Key Features:

• Image Augmentation: Integrated image augmentation techniques to augment the dataset, promoting better generalization and robustness.
• One-Hot Encoding: Applied one-hot encoding for efficient representation of categorical labels, a crucial step in multiclass classification.
• Learning New Topics: Delved into and mastered concepts such as one-hot encoding and various image augmentation techniques during the course of the project.

📚 What I Learned: This project served as a valuable learning experience, enhancing my understanding of:

• Convolutional Neural Networks (CNNs) and their applications in image classification.
• Multiclass classification techniques for interpreting diverse sign language symbols.
• One-hot encoding as a powerful method for handling categorical data.
• Various image augmentation techniques to improve model robustness.

👏 Acknowledgments: Special thanks to the MNIST dataset creators for providing a rich resource for sign language gesture recognition.

🚀 Getting Started: Explore the codebase, delve into the model architecture, and witness the power of CNNs in sign language prediction.

🌟 Contributions: Contributions and feedback are welcome! Feel free to fork the repository, experiment with the code, and share your insights.

Let's empower technology to understand and interpret the richness of sign language! 🤟🤖