Machine Learning DesignPRO

Chapter 11: Training Deep Neural Networks — Stability, Optimization, Regularization, and Transfer Learning

An interview-focused guide to training deep neural networks reliably, covering vanishing and exploding gradients, initialization, activation functions, BatchNorm, gradient clipping, optimizers, learning-rate schedules, regularization, transfer learning, and Staff-level ML system design.

Deepak Mishra·31 August 2026·20 min read