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Phase 7 · Deep Learning Foundations

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Loss Functions

Part of the AI Engineer Roadmap.

Summary

Functions (MSE, cross-entropy) that quantify how wrong a model's predictions are — the single number gradient descent is trying to minimize.

How to Learn This

1Learn when to use MSE (regression) vs. cross-entropy (classification).
2Implement both loss functions from scratch in NumPy.
3Understand how loss shape affects how easily gradient descent can optimize it.

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Forward & Backward Propagation

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Gradient Descent Variants (SGD, Adam)

More topics in Deep Learning Foundations

Neural Network Basics Perceptrons Activation Functions Forward & Backward Propagation Gradient Descent Variants (SGD, Adam)PyTorch Basics TensorFlow/Keras Basics Batch Normalization Dropout & Regularization in DL GPUs & CUDA Basics

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