Deep Learning Optimization Explained for AI Engineers DAY17 скачать в хорошем качестве

Deep Learning Optimization Explained for AI Engineers DAY17

Deep Learning Optimization is the hidden engine behind stable, scalable AI systems. In this lesson, you’ll build a production-level understanding of optimizers, convergence, stability, and large-scale training dynamics. 📌 Timestamps 00:00:00 — Why Optimization Matters in Deep Learning 00:01:35 — Loss Landscapes & Non-Convexity 00:02:27 — Stochastic Gradient Descent (SGD) 00:03:00 — Momentum-Based Methods 00:03:35 — Adaptive Optimizers (Adam, RMSprop) 00:04:12 — Second-Order Methods 00:04:47 — Natural Gradient Methods 00:05:20 — Batch Normalization & Optimization Dynamics 00:05:55 — Learning Rate Schedules 00:06:22 — Warm-Up Strategies & Curriculum Learning 00:06:54 — Saddle Points & Optimization Stagnation 00:07:24 — Generalization Theory & Double Descent 00:07:52 — Large-Scale & Distributed Optimization 00:08:23 — Few-Shot & Meta-Learning Optimization