Foundations of AI/ML Workloads

Explain the difference between AI training and inference workloads.

Describe the forward pass, backward pass, and optimizer step in a neural network training loop.

Identify key computational operations in deep learning (e.g., matrix multiplication, convolution).

Explain the role of batch size in trading off throughput and resource utilization.

Run a pre-existing script to observe the effect of changing batch size on training time and GPU memory.