Skimming the sections on linear models, I was surprised not to see a discussion of model fit, beyond just plotting predicted values against observed ones. Basic predictive models like linear and logistic regression are simple to construct mechanically. It's a substantially more involved task to quantify their degree of fit and, better yet, prove that the methods are optimal and unbiased (in a statistical sense).
Show HN: ML From Scratch – free online textbook
31–40 of 56 posts
Re: Show HN: ML From Scratch – free online textbook
#32I read the title and was somehow was expecting a guide on compiler design implementing some flavor of the the ML language such as Standard ML.
Re: Show HN: ML From Scratch – free online textbook
#33I read the title and was somehow was expecting a guide on compiler design implementing some flavor of the the ML language such as Standard ML.
Yeah, I was excited too. Of all the naming conflicts in our industry, this is one that frequently bites me.
Re: Show HN: ML From Scratch – free online textbook
#34That's a really cool initiative, but I think we disagree on the term "from scratch". Taking a look at the source code, I see you're using sklearn - which is a great tool - but, from scratch, at least for me, implies writing your own code (logistic and linear regression, adaline, perceptron, mlp, knn, kmeans...) I mean, that's how I learned it. But again, congratulations on the initiative.
Re: Show HN: ML From Scratch – free online textbook
#35This looks fantastic! As a bit of a selfish question, have you considered also offering a downloadable epub of this book? I've been trying to keep my long-form digital reading to my eReader, for the sake of not looking at LCDs all day, but that makes web ebooks a bit of a pain due to e-ink not liking scrolling very much.
Good question. I definitely prefer downloadable books myself. I made it in JupyterBook because that was easiest with the executable ipynb files. I'll look into whether I can make it downloadable and update you if so.
Re: Show HN: ML From Scratch – free online textbook
#36That's a really cool initiative, but I think we disagree on the term "from scratch". Taking a look at the source code, I see you're using sklearn - which is a great tool - but, from scratch, at least for me, implies writing your own code (logistic and linear regression, adaline, perceptron, mlp, knn, kmeans...) I mean, that's how I learned it. But again, congratulations on the initiative.
Perhaps I should have been clearer, but the "code" section within each chapter is not "from scratch". The "construction" section is "from scratch" in that it only uses numpy (not scikit learn). The scikit-learn part is just so new users can see how these could be fit in practice.
Re: Show HN: ML From Scratch – free online textbook
#37Skimming the sections on linear models, I was surprised not to see a discussion of model fit, beyond just plotting predicted values against observed ones. Basic predictive models like linear and logistic regression are simple to construct mechanically. It's a substantially more involved task to quantify their degree of fit and, better yet, prove that the methods are optimal and unbiased (in a statistical sense).
I agree though I saw that as outside the scope of this book. I tried to be clear in the introduction that the book is a "user manual" of sorts that simply shows how to construct models, rather than how to decide between them, what the benefits of each are, etc. That information is certainly important but I felt it had been covered more than adequately by books like Elements of Statistical Learning
Re: Show HN: ML From Scratch – free online textbook
#38Looking through the topics, a useful addition would be SVMs.
Re: Show HN: ML From Scratch – free online textbook
#39Nice work. Seems like it focuses on machine learning stuff that's not deep learning. Maybe you could add a section on collaborative filtering.
Re: Show HN: ML From Scratch – free online textbook
#40I read the title and was somehow was expecting a guide on compiler design implementing some flavor of the the ML language such as Standard ML.
I'm sad it's not!