# ✅ Checkpoint: Backpropagation and Model Comparison

By Better Wise

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## Contents

Part of [🧮 Mathematics for Machine Learning 4: Build Core ML Algorithms from Scratch](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch.md)

- **📈 Linear and Cross-Entropy Models from Scratch**
  - [📉 Linear Regression with Normal Equations and Gradient Descent](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/linear-regression-normal-equations-and-gradient-descent.md)
  - [🔍 Gradient Checking with a Finite-Difference Harness](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/gradient-checking-finite-difference-harness.md) (sign-in required)
  - [🎚️ Logistic Regression to Softmax: Cross-Entropy Done Right](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/logistic-regression-to-softmax-cross-entropy.md)
  - [✅ Checkpoint: Linear and Cross-Entropy Models](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/checkpoint-linear-and-cross-entropy-models.md)
- **🔵 Unsupervised Learning from Scratch**
  - [🧭 PCA via SVD: Explained Variance and Component Selection](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/pca-via-svd-explained-variance.md)
  - [🌀 k-means: Lloyd's Algorithm and k-means++ Initialization](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/k-means-lloyd-and-k-means-plus-plus.md)
  - [🔔 Gaussian Mixture Models with Expectation Maximization](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/gaussian-mixture-models-expectation-maximization.md)
  - [✅ Checkpoint: PCA, k-means and GMM](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/checkpoint-pca-kmeans-and-gmm.md)
- **🧠 Backpropagation and Benchmark Capstone**
  - [🔗 Backpropagation for a One-Hidden-Layer MLP](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/backpropagation-for-one-hidden-layer-mlp.md)
  - [📊 Cross-Validation and a Fair Baseline Comparison Protocol](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/cross-validation-and-baseline-comparison-protocol.md)
  - [🏁 Capstone: NumPy Implementations vs Library Baselines](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/capstone-numpy-vs-library-baselines.md) (premium content)
  - [✅ Checkpoint: Backpropagation and Model Comparison](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/checkpoint-backpropagation-and-model-comparison.md) (premium content) — this lesson
- [Course cheatsheet](https://everwise.study/courses/mathematics-for-machine-learning-4-build-core-ml-algorithms-from-scratch/cheatsheet.md) (premium content)