2 days ago
- Parallelization speeds up training by processing batches of inputs simultaneously using GPUs, SIMD, and multi-core CPUs.
- Using optimized matrix multiplication libraries like Eigen can yield dramatic speedups (e.g., 320x).
- Advanced optimizers like ADAM combine momentum and adaptive learning rates to converge much faster and avoid local minima.
- Hyperparameters (learning rate, batch size, momentum) are critical and often require extensive tuning; they differ from learned parameters.
- Inspection tools like SQLiteWriter allow logging and visualization of parameter values and gradients over time.
- ADAM requires storing momentum and velocity tensors for each parameter, and parallelization uses a shared-nothing architecture with thread copies.