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beginner~6h

Deep Learning & Gradient Descent

Learn loss functions, gradients, training iterations, and how models adjust weights to reduce errors.

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▶📚 Prerequisites(1)

🎓 Learning objectives

  • •Explain what a Loss Function (like MSE) measures
  • •Understand how Gradient Descent steps weights down the error curve
  • •Define Learning Rate and explain the risk of overshooting

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📂 Subtopics

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Gradient Descent Variants: SGD, Momentum and Adam

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Overfitting and Regularization: Dropout, Weight Decay and Early Stopping

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Batch Normalization: Why Deep Networks Are Hard to Train and How It Helps

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Training Tips and Tricks: LR Scheduling, Gradient Clipping and Mixed Precision

Related concepts

neural-networksprobability-basics

Next to learn

llm-training →finetuning-peft →
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