Learn to classify hand sign language alphabets using deep learning in this 22-minute tutorial. Explore the MNIST sign language dataset, which includes 25 classes of hand gestures representing the alphabet (excluding Z). Discover data preparation techniques, normalize the dataset, and create a deep learning model for accurate classification. Follow along as the instructor guides you through loading data, analyzing data distribution, compiling the model, and evaluating training accuracy. Access the complete code on GitHub and download the dataset from Kaggle to practice implementing this sign language classification system.
Overview
Syllabus
Intro
Data preparation
Loading data
Data distribution
Normalize data
Create a model
Compile model
Results
Training Accuracy
Conclusion
Taught by
DigitalSreeni
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