Implement Machine Learning Algorithms From Scratch (NumPy Only)

Proof required

Submit: a public GitHub repository (or Colab notebook) containing your NumPy implementations with training and evaluation report showing your implementation's metrics vs scikit-learn's on the same dataset; and a 200-word explanation of why gradient descent converges and what the learning rate controls. A data scientist or ML engineer must confirm the implementations are correct and the comparison is valid.

What gets checked

• Implementations use only NumPy — no scikit-learn or ML frameworks for the core algorithm
• Comparison to scikit-learn shows metrics within 1% on identical inputs — not just 'similar' results
• A data scientist or ML engineer has confirmed the implementations are correct

Common mistakes

• Using scikit-learn's Lasso or Ridge as a 'scratch implementation' — the proof requires manual gradient descent
• Comparison to scikit-learn on different datasets or preprocessing — the comparison must be on identical inputs

Resources

What a verifier looks for

• Are the implementations using only NumPy — no scikit-learn for the core algorithm?
• Is the scikit-learn comparison on identical inputs and preprocessing steps?
• Does the gradient descent explanation correctly describe why the learning rate matters?

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Proof required

Submit: a public GitHub repository (or Colab notebook) containing your NumPy neural network with training script, accuracy plot, and confusion matrix; a handwritten or typed derivation of the backpropagation equations for at least two layers; and a comparison to a PyTorch baseline on the same dataset. A data scientist or ML engineer must confirm the implementation is correct and the accuracy is measured on the test set.

What gets checked

• Implementation uses only NumPy — no automatic differentiation libraries
• Backpropagation derivation is written out for at least two layers — not just code with gradient variables
• Test-set accuracy of at least 95% on MNIST — not training-set accuracy

Common mistakes

• Using PyTorch autograd for the backpropagation — the proof requires manual gradient derivation
• Accuracy measured on the training set rather than the test set — generalisation is what matters

Resources

What a verifier looks for

• Is the neural network implemented in NumPy only — no automatic differentiation?
• Is the backpropagation derivation written out for at least two layers?
• Is the accuracy measured on the test set — not the training set?

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Proof required

Submit: a recording or transcript of a live technical review with a data scientist or ML engineer; the portfolio repositories; and documentation of the novel algorithm question posed, your on-the-spot sketch, and the reviewer's assessment of whether the approach is sound.

What gets checked

• Review was live — not an async written exchange
• A novel algorithm design question was posed that was not covered in the portfolio
• Reviewer's assessment of the on-the-spot sketch is documented

Common mistakes

• Live review that becomes a portfolio walkthrough without novel questions from the reviewer
• Novel question that can be answered by recalling the portfolio — must be genuinely new

Resources

What a verifier looks for

• Was the review live — not async?
• Was the novel question genuinely new — not answerable by recalling the portfolio?
• Is the reviewer's assessment of the sketch documented?

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