Vectorization and Efficient Neural Network Computation
Understand how matrix operations enable efficient parallel computation in neural networks.
Vectorization is the practice of processing multiple data samples simultaneously using matrix operations rather than loops. Instead of computing predictions for one sample at a time, vectorized code computes predictions for entire batches using matrix multiplication. This is orders of magnitude faster because modern hardware (GPUs) is optimized for matrix operations. When you pass a batch of 32 samples to a neural network, the computation happens in parallel across all samples. Understanding vectorization helps you appreciate why neural networks can process millions of samples efficiently and why frameworks like TensorFlow are designed around matrix operations rather than explicit loops.
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