Tag: Hyperparameter Tuning
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AutoML Is Overrated: Why Manual Tuning Wins for Beginners
AutoML promises fast results but hides the lessons beginners need most. Manual tuning teaches bias-variance tradeoffs AutoML skips โ here's why.
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PPO Entropy Decay Bug: Why Exploration Dies at 500K Steps
Your PPO agent flatlines at 500K steps because entropy coefficient decay silently kills exploration. Here's the adaptive fix that saved my Ant-v4 runs.
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Optuna NAS: 40 Trials to Match Hand-Tuned Architecture
40 Optuna trials matched 3 weeks of manual architecture tuning. Here's how to set up TPESampler and HyperbandPruner so your NAS actually converges.
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PPO Training Diverges After 1M Steps: Clipping & LR Fixes
PPO training collapse after 1M steps? Learn how gradient clipping and learning rate schedules prevent policy divergence in deep RL implementations.
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W&B Sweeps: Bayesian vs Grid Search Benchmark (ResNet-18)
Bayesian search hit 91% in 28 runs. Grid needed 64. Real benchmarks, ResNet-18, CIFAR-10โsee which strategy wins.