Sparse Neural Networks

Interactive Notebook

Summary

This chapter demonstrates how GraphBLAS enables sparse neural network computation:

• Neural Network Fundamentals - The building blocks of machine learning and AI systems
• Dense vs Sparse Networks - How sparse representations overcome memory limitations of dense neural networks
• Sparse Matrix Representation - Encoding neural network weights as sparse matrices
• Inference with GraphBLAS - Performing forward propagation using sparse matrix-vector multiplication
• Activation Functions - Applying non-linear transformations using GraphBLAS apply operations
• Scalability - Building larger networks than possible with dense approaches