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Ask HN: In 2022, what is the proper way to get into machine/deep learning?

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Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#131

Post by alanfranz »

liquorice wrote: post #102
alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

They are probably not silly mistakes. Label encoding can be very useful for tree based models when the categories are ordinal, or when there are a high amount of categories.

They are most of the times. You get a prediction with a meaningless float (unless the categories are ordinal, which isn’t so common), and categories can change their assigned number (happens in lots of analyses) at every run since they’re not properly sorted. Crawl a few notebooks, I spotted that error quite often.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#132

Post by alanfranz »

nkzd wrote: post #99
alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

I am a CS major but I was always bad at math. Can you recommend your favorite resources for learning probability and statistics basics?

“Introduction to statistical learning” is probably the good goto resource to start with. There’s a decent open source book on probability which I use when I need more in-depth understanding, but I don’t remember the title right now.

One clarification: you don’t need an extreme understanding of stats, probability, linear algebra, imho. If you already took college level classes, you’re likely to be fine.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#133

Post by Kaapeine »

jokethrowaway wrote: post #126
mrcartmeneses wrote: post #61

Earlier quoted context omitted.

I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. I’m interested in “flow capture based on power relationships”. Do you have any recommended reading on this?

Every large organisation tends to be like a small government. Inefficient, drown in politics and unable to change. There are exceptions - where someone principled dictator impose a VC style model where teams basically become independent startup and die or succeed. 100 fails, one becomes the next revenue maker for the company. That's how AWS was born.

That sounds a lot like the DARPA model too.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#134

Post by jll29 »

> what are the best resources for a CS student/decent programmer to get into the field of ML and DL on their own

It would be helpful to know more about your background and motivations.

Are you currently enroled in a Bachelor of Science (B.Sc.) full time stdy program at a university, and your goal is to be a research scientist (either staff scientist or professor or research fellow) in the area of machine learning?

If this is true, does your university offer a Master's program in Machine Learning, or are your grades such that you could apply for such a program elsewhere after completing your first degree? You could then enter a Ph.D. programm in machine learning itself, or in computer science with an applied ML topic such as ML for NLP (Natural Language Processing) or ML for IR (Information Retrieval = search engines) or ML for robotics etc. The choice of doctoral advisor and Ph.D. topic will steer you towards a particular direction, in which you can then find employment to conduct research under the direction of others, and potentially, become a research group leader yourself after gaining the necessary experience. Time: M.Sc.: 1-2 years; Ph.D.: 3-8 years; postdoctoral/pre-tenure time: e.g. 2-k years, depending on ability and luck/timing). It's a lot of fun to get paid for doing science, so I chose that path (but with multiple deviations due to startups and industry jobs along the way).

The more people know, the easier it is to recommend you useful materials.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#135

Post by alanfranz »

spywaregorilla wrote: post #112
alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

While stats and probability are very good, I can't say you need more than a good 101 level course for either. Really you're just looking for some good reasoning skills about how distributions and probability works. This example: > It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous var…

You need to understand that a category cannot be magically transformed into a float. Yeah, maybe not the best example on my part.

For the 101 level, I agree; I’d say you need a good understanding of basic probs and stats, rather than a vague understanding of advanced topics.

If you can spot when somebody says something about a dataset, the assertion is true only if the data is normally distributed, but there’s no checking about the actual distribution, you’re probably good to go.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#136

Post by WASDx »

jamesdhutton wrote: post #11

Andrew Ng's machine learning course on Coursera is a good introduction to the theory. https://www.coursera.org/learn/machine-learning

Not sure if it's the same material but there is a course by him on youtube as well: https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkY...

I've watched every video in that playlist. He is a fantastic teacher.

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#137

Post by [deleted] »

wokwokwok wrote: post #30
santiagobasulto wrote: post #22

Can I suggest a longer, but (I think) better route? Try the Data/ML Engineer route. Instead of going directly into ML, try to work as a “supporter” of those doing ML. There’s a HUGE gap there, specially if you’re a good programmer. There are a lot of people in the “pure” ML space, people with science background, with phDs, etc. But there’s not enough people to support them: taking their models to producing, building…

> I think it can yield the highest success rate. At what? If you want to be an SRE for a data platform, sure, but this pretty thankless work: - cleaning up dodgy data - cleaning up behind low code data pipelines and other painful integration work with systems that suck and sometimes just don’t work (like PowerBI). - cleaning up behind data scientists that create models in arcane and imaginative ways and expect you to…

[deleted]

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#138

Post by abbusfoflouotne »

alanfranz wrote: post #47

My 2c (not exhaustive for what you want to do, probably): 1) Get some statistics/probability basics. It's full of people (you can see a lot of analyses on Kaggle) that "do machine learning" but make very silly mistakes (e.g. turn categorical data into a float and use it as a continuous variable when training a model). 2) take a look at traditional machine learning approaches. Nowadays you're swamped by DL (a lot of g…

THWG

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#139

Post by chrstr »

bitL wrote: post #42
LeetHacks wrote: post #34

Earlier quoted context omitted.

It highly depends. I was hired for a small research group that didn't have a product in production. Got hired for programming, was on the table discussing and contributing to research within a couple months without any background in ML.

In smaller teams/companies one gets to wear multiple hats. However, the term "Data engineer" was specifically created by/for ML folks to get rid of unpleasant repetitive work that has to be done but nobody looks forward to it.

> However, the term "Data engineer" was specifically created by/for ML folks to get rid of unpleasant repetitive work that has to be done but nobody looks forward to it.

This may indeed be how the term "data engineer" is used sometimes, but I have my doubts that it was originally created with this meaning. Not really sure where/when the term "data engineer" was actually created, but ICDE started in 1984 [1] and the Data Engineering Bulletin was renamed in 1987 [2] (from "Database Engineering"). It seems likely that the term "data engineer" has also been used since at least then.

Of course ML did also already exist then, but it's certainly a while before the current "big data" / "deep learning" time. And regarding the topics considered "data engineering" at that time, this is from the foreword of the December 1987 issue of the Data Engineering bulletin:

> The reasons for the recent surge of interest in the area of Databases and Logic go beyond the theoretical foundations that were explored by early work [...] and include the following three motivations:

> a) The projected future demand for Knowledge Management Systems. These will have to combine inference mechanisms from Logic with the efficient and secure management of large sets of information from Database Systems.

Which sounds just as relevant today as it did back then. It also does sound like a rather challenging task, and not exactly like "unpleasant repetitive work". Or at least not any more repetitive than: change some model parameters / retrain model / evaluate results / repeat ;)

[1]: https://ieeexplore.ieee.org/xpl/conhome/1000178/all-proceedi...

[2]: http://sites.computer.org/debull/bull_issues.html

Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?

#140

Post by freedomben »

ford wrote: post #117

I work in ML - I might make 3 buckets for ML careers right now: 1. ML/DL Researcher 2. Data scientist - 20/80 engineering vs modelling 3. ML Eng - 50/50 (or 70/30) engineering vs modelling People suggesting working in engineering to support ML are right that there's a lot of demand, but it's not what you're asking for. Becoming an ML/DL researcher working on novel techniques or new models will be hard without academi…

Would you (or somebody else) mind comparing/contrasting Data scientist/ML Eng a little more? I'm not sure I understand the difference (and perhaps like many roles/titles in our industry the line is blurry).

Never mind, I mentally flipped the numbers. I read 20/80 and 30/70 but it's actually 20/80 and 70/30. IOW, Data scientist spend a lot more time modeling, and ML eng spend a lot more time engineering. makes a lot of sense. I'll post this comment anyway in case it helps someone else.

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