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

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

#101

Post by fxtentacle »

fxtentacle wrote: post #100
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…

If you want to raise your salary from $10 to $20 per hour, playing with existing models is the way to go. If you want to make serious money solving real problems, take the time to learn about automated differentiation and all the related mathematics about how gradients flow backwards through the network. But like the coding slave (great nick BTW) said, first play a bit, then learn how it works. Image transformation G…

https://www.tensorflow.org/tutorials/generative/cyclegan Just click [Run in Google Colab] on the top left and start evaluating the code blocks.

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

#102

Post by fxtentacle »

tensor wrote: post #57
codingslave wrote: post #13

Earlier quoted context omitted.

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…

What usually happens is that people get something working, think they now know ML, but don't even generally know enough to understand the things they did wrong, and never end up getting to the theory. The best approach is to learn both concurrently. Learn some theory, apply it and understand that applications including pitfalls, then learn a bit more and repeat. Incremental learning with a solid base. It's fun to hat…

Sadly, you are 100% correct. I see the same problems over and over in newly published AI research papers.

That said, playing for 1-2 weeks might be a good start towards getting motivated for learning the difficult and dry theory needed to excel in this field.

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

#103

Post by psv1 »

fantispug wrote: post #92
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…

If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice). But it's ok to start using libraries and fitting models without understanding how they work deeply, and coming back to these books later (just make…

> If you like books and you want to deeply understand ML techniques I'd suggest jumping straight into "Introduction to Statistical Learning" and only learning calculus/stats/matrix methods (linear algebra) as you need them (you really don't need much from them in practice).

This doesn't work. ISL is good, but it aims to be accessible by excluding most of the math. So if you go over it, you'll neither "deeply understand ML techniques", nor will you encounter enough math that you can learn along the way as you suggest.

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

#104

Post by fxtentacle »

rramadass wrote: post #86
jimbokun wrote: post #43

Earlier quoted context omitted.

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

Exactly! One cannot do ML without some basic theoretical knowledge of Statistics and Probability. This gives you the What and the Why behind everything . GI-GO is more true of ML than other disciplines. The techniques used are so opaque that if you don't know what you are doing, you can never trust the results.

One thing that made the Uber fatality possible was their over-confidence in their AI, which they apparently did not fully understand. They considered it unnecessary and disabled the car-integrated emergency collision breake system ...

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

#105

Post by martingoodson »

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…

>Also, I will throw in my consiracy theory that most ML researchers and such push the theory/deep stats requirement as a form of gatekeeping.

Learning the fundamentals of a field is supposed to be gatekeeping. It's what stops you from making stupid mistakes. The field of ML is littered with horrible errors made by people who don't know the fundamentals.

Please don't follow this terrible advice.

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

#106

Post by stared »

kartoshechka wrote: post #10
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…

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…

“Scientists start out doing work that's perfect, in the sense that they're just trying to reproduce work someone else has already done for them. Eventually, they get to the point where they can do original work. Whereas hackers, from the start, are doing original work; it's just very bad. So hackers start original, and get good, and scientists start good, and get original.” - Paul Graham in Hackers and Painters

Which, well, I use as an opening quote to my intro to deep learning, https://github.com/stared/thinking-in-tensors-writing-in-pyt....

BTW: While information theory is everywhere, I have to yet see where measure theory makes a practical impact on practical deep learning. The importance of pure math for practical machine learning is highly overrated (and I speak as someone who did study that).

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

#107

Post by cameronbrown »

martingoodson wrote: post #105
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…

>Also, I will throw in my consiracy theory that most ML researchers and such push the theory/deep stats requirement as a form of gatekeeping. Learning the fundamentals of a field is supposed to be gatekeeping. It's what stops you from making stupid mistakes. The field of ML is littered with horrible errors made by people who don't know the fundamentals. Please don't follow this terrible advice.

Doesn't it depend on what you're trying to do?

I think there's a huge difference between research and learning enough to scrap something together for a hobby project. The deep maths can come later.

I don't need to study compiler theory to use GCC.

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

#108

Post by joaogui1 »

Machine Learning:

* https://www.youtube.com/watch?v=UzxYlbK2c7E: Andrew Ng's machine Learning course, the recommended entry point by most people

* https://mlcourse.ai/ : More kaggle focused, but also more modern and has interesting projects

Do both courses simultaneously, take good notes, write useful flashcards, and above all do all the exercises and projects

Deep Learning

* https://www.fast.ai/ - Very hands-on, begin with " Practical Deep Learning for Coders" and then "Advanced Deep Learning for coders"

* https://www.coursera.org/specializations/deep-learning : More bottom-up approach, helps to understand the theory better

Do those two courses in parallel (you can try 2 weeks of coursera followed by one of fastai in the beginning, and then just alternate between them), take notes, write good flashcards and above all do the exercises and projects.

After that you will be done with the beginning, your next step will depend on what area interested you the most, and getting way too many resources right now can be extremely confusing, so I would recommend doing a follow-up post after you worked through the above resources. Also as non-ML stuff I recommend Scott Young's Ultralearning and Azeria's self improvement posts (https://azeria-labs.com/the-importance-of-deep-work-the-30-h...)

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

#109

Post by rramadass »

cameronbrown wrote: post #107
martingoodson wrote: post #105

Earlier quoted context omitted.

>Also, I will throw in my consiracy theory that most ML researchers and such push the theory/deep stats requirement as a form of gatekeeping. Learning the fundamentals of a field is supposed to be gatekeeping. It's what stops you from making stupid mistakes. The field of ML is littered with horrible errors made by people who don't know the fundamentals. Please don't follow this terrible advice.

Doesn't it depend on what you're trying to do? I think there's a huge difference between research and learning enough to scrap something together for a hobby project. The deep maths can come later. I don't need to study compiler theory to use GCC.

Your analogy is wrong i.e. you are comparing apples to oranges. ML is very different from other "normal" computation systems.

* Non-ML: Input + {Rules} = Output

* ML: Input + Output = {Rules}

where "{Rules}" = Infinite set of possible "Programs" each of which is a trace through a very large state space of variables.

In the first case, we humans use all our ingenuity to write the program and tweak it to get the right results. We already know the difficulties involved in writing "correct" programs but have mastered it to some extent.

In the second case, you cannot do that. Your "Programs" are derived by the system and encoded in numbers. How in the world do you even know that your encodings are correct? This is why you need the techniques of Mathematics to transform (eg. Linear Algebra) and constrain (eg. Inferential Statistics/Probability) the output "Rules" so you can have some measure of confidence in it. This is the fundamental challenge inherent in ML.

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

#110

Post by rramadass »

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…

As others have pointed out, terrible wrong-headed advice to ignore all "Theory". ML cannot be studied by a scatter-shot approach but needs a systematic plan with Theory first followed by Practice and constant iteration between the two.
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