Optimization, Regularization, GPUs

In this episode, we explore the three engineering pillars that made modern deep learning possible: advanced optimization methods, powerful regularization techniques, and GPU-driven acceleration. While the core mathematics of neural networks has existed for decades, training deep models at scale only became feasible when these three domains converged. We examine how optimizers like SGD with momentum, RMSProp, and Adam navigate complex loss landscapes; how regularization methods such as batch normalization, dropout, mixup, label smoothing, and decoupled weight decay prevent overfitting; and how GPU architectures, CUDA/cuDNN, mixed precision training, and distributed systems transformed deep learning from a theoretical curiosity into a practical technology capable of supporting billion-parameter models.This episode covers:• Gradient descent, mini-batching, momentum, Nesterov acceleration • Adaptive optimizers: Adagrad, RMSProp, Adam, and AdamW • Why saddle points and sharp minima make optimization difficult• Cyclical learning rates and noise as tools for escaping poor solutions• Batch norm, layer norm, dropout, mixup, and label smoothing• Overfitting, generalization, and the role of implicit regularization • GPU architectures, tensor cores, cuDNN, and convolution lowering • Memory trade-offs: recomputation, offloading, and mixed precision • Distributed training with parameter servers, all-reduce, and ZeROThis episode is part of the Adapticx AI Podcast. You can listen using the link provided, or by searching “Adapticx” on Apple Podcasts, Spotify, Amazon Music, or most podcast platforms.Sources and Further ReadingAll referenced materials and extended resources are available at:https://adapticx.co.uk