Deep Learning Optimization Explained for AI Engineers DAY17
Автор: Life Decode
Загружено: 2026-02-15
Просмотров: 43
Описание:
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
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