Day 25 of Machine Learning
Worked a lot on my Haskell neural net, also started another ML-related Haskell project.
Post linkDay 24 of Machine Learning
Learned more Haskell and started writing an MLP in it (blog post about that soon).
Post linkDay 20 of Machine Learning
Friendship ended with Python, Haskell is my new friend.
Post linkDecision Trees - Day 18 of ML
"Machine learning is just a bunch of if-statements under the hood." ...maybe it is!
Post linkMore Properties of Matrices - Day 17 of ML
Continuing with linear algebra: LU decomposition, matrix inverses, transposes and permutation.
Post linkFeature Engineering - Day 15 of ML
Briefly going over some ways to optimize our datasets before feeding them to ML models.
Post linkSupport Vector Machines - Day 14 of ML
Today we're exploring an elegant classification model - the Support Vector Machine (SVM).
Post linkDay 13 of Machine Learning
Did my first Kaggle competition and looked further into SVMs
Post linkDay 12 of Machine Learning
Didn't do anything basically. A plan for the upcoming days.
Post linkDay 9 of Machine Learning
Didn't write anything down. Did some exercises but slacked off otherwise
Post linkThe Start of The Linear Algebra Grind - Day 7 of ML
Exploring systems of linear equations graphically.
Post linkGradient Descent - Day 6 of ML
Briefly covering a fundamental ML optimization algorithm.
Post linkBasic Probability Theory & Bayes' Theorem - Day 5 of ML
Continuing on statistics and probability.
Post linkGetting Started With Probability and Statistics - Day 4 of ML
First part of the code-centric approach to stats series.
Post linkLinear & Logistic Regression - Day 3 of ML
What if you wanted to fit a line into data?
Post linkIntegrals & Riemann sums - Day 2 of ML
Looking into integrals and Riemann sums.
Post linkBrushing Up Calculus - Day 1 of ML
Revisiting what I've studied in calculus before - limits and derivatives.
Post linkBecoming a Cracked Engineer - Day 0 of ML
The start of my plan to become a machine learning engineer.
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