Ask HN: Full-on machine learning for 2020, what are the best resources? • Hacker News Board

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#41

Post by psv1 » Tue, Dec 31, 2019, 11:07 PM UTC

Good free resources:

- MIT: Big Picture of Calculus

- Harvard: Stats 110

- MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material).

This gives you a math foundation. Some books more specific to ML:

- Foundations of Data Science - Blum et al.

- Elements of Statistical Learning - Hastie et al. The simpler version of this book - Introduction to Statistical Learning - also has a free companion course on Stanford's website.

- Machine Learning: A Probabilistic Perspective - Murphy

That's a lot of material to cover. And at some point you should start experimenting and building things yourself of course. If you'are already familiar with Python, the Data Science Handbook (Jake Vanderplas) is a good guide through the ecosystem of libraries that you would commonly use.

Things I don't recommend - Fast.ai, Goodfellow's Deep Learning Book, Bishop's Pattern Recognition and ML book, Andrew Ng's ML course, Coursera, Udacity, Udemy, Kaggle.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#42

Post by jansc » Tue, Dec 31, 2019, 11:18 PM UTC

jansc wrote: post #35

Humblebundle has a bundle of machine learning books right now: https://www.humblebundle.com/books/python-machine-learning-p... I'm considering buying this bundle. Any of these books you would recommend?

I'm not affiliated with humblebundle in any way, and this was a genuine question. I know that the packt books are not best quality, but if one these books is a good introduction to practical ML, I would consider it a good deal. In my opinion much better than googling algorithms and tutorials and visiting 10s or 100s of sites full of ads and ad trackers to find a suitable algorithm for a given problem. Reading an EPUB on my daily commute sounds much better and works offline.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#43

Post by jimbokun » Tue, Dec 31, 2019, 11:19 PM UTC

codingslave wrote: post #13

kartoshechka wrote: post #10

Earlier quoted context omitted.

I hope you're trolling because this is a guaranteed way to climb a peak of stupidity [1]. If OP is determined to get a bit deeper than 30 min guides on Medium, there is sure theory to learn. But it is merely second year of college, and probably you would like to skip Kolmogorov axiomatics and measure theory, it won't hurt your understanding of bleeding edge researches. [1] https://en.m.wikipedia.org/wiki/Dunning%E2%8…

My opinion is that the theory starts to make sense after you know how to use the models and have seen different models produce different results. Very few people can read about bias variance trade off and in the course of using a model, understand how to take that concept and directly apply it to the problem they are solving. In retrospect, they can look back and understand the outcomes. Also, most theory is useless…

The danger is throwing something into production without understanding bias and variance, overfitting (or other important concept) with potentially disastrous results.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#44

Post by frequentnapper » Tue, Dec 31, 2019, 11:35 PM UTC

tridenrake wrote: post #20

kartoshechka wrote: post #17

Earlier quoted context omitted.

I personally started with Kaggle competitions and lots of googling (duckduckgoing right?), but quite quickly hit the wall of not understanding, I felt like a mindless creature who makes a decision based on couple of guides out there. Watching lectures from Andrew Ng, reading some books helped a lot, but I can't see a reason why one doesn't wanna start with theory. It's no gold and glitter, and no one promised you tha…

I guess his point is to tackle it from a top-down approach. For me, that's how I am breaking ground in my ML study. I tried Andrew Ng's course, I didn't understand a thing. Then I tried Kaggle's mini-course. It kickstarted me into ML and motivated me to learn the theory as I go. For example, when I got to apply Random Forest Regressor, I went to Wikipedia and tried to read on it. Got some idea. And the progress is go…

Same here. I tried Andrew Ng's course a few times ever since it launched a few years back but I could only get through half of it. Fast ai makes more sense to me and I've picked up a decent amount of concepts where I can now go back and feel confident enough to tackle theory.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#45

Post by vowelless » Tue, Dec 31, 2019, 11:36 PM UTC

psv1 wrote: post #41

Good free resources: - MIT: Big Picture of Calculus - Harvard: Stats 110 - MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Has…

Why don't you recommend fast.ai and kaggle?

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#46

Post by elamje » Tue, Dec 31, 2019, 11:37 PM UTC

codingslave wrote: post #6

Honestly, skip all of the courses. Pick a problem to solve, start googling for common models that are used to solve the problem, then go on github, find code that solves that problem or a similar one. Download the code and start working with it, change it, experiment. All of the theory and such is mostly worthless, its too much to learn from scratch and you will probably use very little of it. There is so much ml cod…

Just go for it. Learning the math just helps you understand it’s not magic, like learning to program helps you understand computers aren’t magic.

As someone that learned a good bit of the math and implemented NN code with backprop from scratch, I agree with the parent. To learn the math and get better results than cutting edge ML researchers would be as likely as winning the lottery.

As an exercise, the math is fun to learn and not terribly complicated for backprop type of stuff.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#47

Post by joshvm » Tue, Dec 31, 2019, 11:44 PM UTC

psv1 wrote: post #41

Good free resources: - MIT: Big Picture of Calculus - Harvard: Stats 110 - MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Has…

Bear in mind Elements of Statistical Learning is a grad-level text. I would never recommend that to a beginner to the field over an Introduction to Statistical Inference, by the same authors.

Geron Aurelien's Oreilly book is great - Hands-On Machine Learning with Scikit-Learn and TensorFlow. Get the second edition which covers Tensorflow 2.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#48

Post by vowelless » Tue, Dec 31, 2019, 11:45 PM UTC

A lot of good advice here.

One thing I would add is replicate a couple of ML papers. It can help develop a lot of intuition about the specific area.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#49

Post by psv1 » Tue, Dec 31, 2019, 11:47 PM UTC

joshvm wrote: post #47

psv1 wrote: post #41

Good free resources: - MIT: Big Picture of Calculus - Harvard: Stats 110 - MIT: Matrix Methods in Data Analysis, Signal Processing, and Machine Learning If any of these seem too difficult - Khan Academy Precalculus (they also have Linear Algebra and Calculus material). This gives you a math foundation. Some books more specific to ML: - Foundations of Data Science - Blum et al. - Elements of Statistical Learning - Has…

Bear in mind Elements of Statistical Learning is a grad-level text. I would never recommend that to a beginner to the field over an Introduction to Statistical Inference, by the same authors. Geron Aurelien's Oreilly book is great - Hands-On Machine Learning with Scikit-Learn and TensorFlow . Get the second edition which covers Tensorflow 2.

You're right about ESL, that's why I started the list with some more fundamental material. Also, +1 for Aurelien's book, it's really good; I didn't know he had a revised edition for TensorFlow 2.

Re: Ask HN: Full-on machine learning for 2020, what are the best resources?

#50

Post by jamesxv7 » Tue, Dec 31, 2019, 11:50 PM UTC

I'm impressed by the responses generated in this conversation. My expectation was to get several links and start browsing each one of them. However, many have agreed that the best way is to start with a specific example and start creating a model. Many times I have tried to answer that same question, "which model to apply"? How do I know I'm not re-inventing the wheel?