Checkpoint: Backpropagation and Model Comparison
This lesson includes a 10-question quiz.
About this course
Part of Mathematics for Machine Learning 4: Build Core ML Algorithms from Scratch — Turn three courses of math into working code: derive and implement regression, PCA, clustering, EM and backpropagation from scratch, finishing with a project that benchmarks your models.
- Level
- Advanced
- Goal
- Build a project
- Language
- English
- Focus
- from-scratch implementations gradient derivations PCA and SVD EM and clustering backpropagation
- Prior knowledge
- Linear algebra: matrices, eigenvalues, SVD, Calculus: gradients and chain rule, Probability: distributions and maximum likelihood, Python and NumPy
- Practice
- Exercises + capstone project
- Time
- ~5.7h
- Voice
- a pragmatic ML engineer who refuses to let you write a line of NumPy until the gradient is derived on paper
- Not covered
- PyTorch and TensorFlow, Large-scale distributed training, Hyperparameter tuning platforms
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