Deep Learning - Sign Language Classification
This project explores deep learning techniques for image classification and neural network modeling. The main focus is on developing a sign language alphabet recognition system using Sign Language MNIST dataset, where the goal is to classify static hand signs representing letters A-Z (excluding J and Z which require dynamic movements). The project also includes a framework for neural network regression/classification on the Heart Disease UCI dataset. The project demonstrates multiple approaches: custom CNN architectures built from scratch, transfer learning with pre-trained models (VGG16), data augmentation techniques (RandomFlip, RandomRotation, RandomZoom, RandomTranslation, RandomCrop), and hyperparameter optimization using GridSearchCV.
Overview
This project explores deep learning techniques for image classification and neural network modeling. The main focus is on developing a sign language alphabet recognition system using Sign Language MNIST dataset, where the goal is to classify static hand signs representing letters A-Z (excluding J and Z which require dynamic movements). The project also includes a framework for neural network regression/classification on the Heart Disease UCI dataset. The project demonstrates multiple approaches: custom CNN architectures built from scratch, transfer learning with pre-trained models (VGG16), data augmentation techniques (RandomFlip, RandomRotation, RandomZoom, RandomTranslation, RandomCrop), and hyperparameter optimization using GridSearchCV.
Key Features
Custom CNN architectures built from scratch for sign language classification
VGG16 transfer learning with pre-trained ImageNet weights
Data augmentation with 7+ techniques (flip, rotation, zoom, translation, crop)
Hyperparameter tuning with GridSearchCV
Model checkpointing and early stopping
24-class classification (letters A-Z excluding J, Z)
Neural network framework for tabular data (Heart Disease UCI)
Multiple solution approaches (3 different implementations)
Model persistence and class mapping preservation
Comprehensive evaluation metrics (accuracy, precision, recall, F1-score)
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pages.portfolio.projects.deep_learning_sign_language.features.11
Technical Highlights
Developed custom CNN architectures for sign language recognition
Implemented VGG16 transfer learning with custom classification head
Applied comprehensive data augmentation techniques
Performed hyperparameter optimization with GridSearchCV
Created neural network framework for tabular data analysis
Achieved robust model performance with checkpointing and early stopping
Challenges and Solutions
Image Classification Complexity
Addressed sign language recognition requiring understanding of hand shapes and positions using CNN architectures
Limited Training Data
Implemented data augmentation techniques to artificially increase dataset size and improve generalization
Overfitting
Used dropout layers, early stopping, and data augmentation to prevent model memorization
Hyperparameter Selection
Applied GridSearchCV for systematic hyperparameter search and cross-validation
Transfer Learning Adaptation
Adapted pre-trained VGG16 model to sign language domain with custom classification head and fine-tuning
Computational Resources
Optimized architectures with efficient designs, model checkpointing, and early stopping for resource management
Technologies
Deep Learning
Neural Networks
Data Augmentation
Data Processing
Visualization
Optimization
Environment
Project Information
- Status
- Completed
- Year
- 2024
- Architecture
- Multi-Solution Deep Learning Project with Custom CNN, VGG16 Transfer Learning, and Neural Network Framework
- Category
- Data Science