Lots of good advice here, but I'd put Google's free course, "AI for all humans: A course to delight and inspire!" out there as a wonderful entree: https://cloud.google.com/blog/topics/developers-practitioner...
Ask HN: In 2022, what is the proper way to get into machine/deep learning?
141–150 of 211 posts
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#142Earlier quoted context omitted.
What? A lot of the hard part isn't the model, and especially in a world where bert, xgboost, optuna, pytorch, etc have solved much of the classic problem and forced 'real' DS to specialize on either the business consulting side (not math/engineering) or theory side (barely implemented). The rebrand of 'data analyst' (SQL, powerbi, . ..) to 'data scientist' by even top tech companies underscores this. It's not yet to…
NeurIPS paper, not neuroips paper
[deleted]
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#143Earlier quoted context omitted.
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 D…
Data engineering jobs named as such started to pop up only in the past few years, coinciding with Map Reduce/Spark availability. I wouldn't be surprised if it was re-introduced by one of the companies developing those systems to distinguish themselves (like Databricks, Cloudera etc.), a sort of a marketing. In the past we had DBAs, now DBA + DevOps + unspecified everything morphed into data engineering.
I used to be a member of SIGMOD and the "data engineering" you mentioned was just an academic term.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#144Follow the HuggingFace Colab notebooks. They are well-written and language-related AIs are a great way to get started because you'll naturally have a feeling for what it should produce. Afterwards, do a statistics class. Most algorithms these days are based on softmax, meaning the cross-entropy between two discrete/continuous probability distributions. There's a lot of choice in which distribution to use to model wha…
ASR is one of the hardest tasks for a novice to ML/DL to do.
In my opinion, it's still A LOT easier than Optical Flow, Depth Recognition, or Robot Navigation... So I'd say:
- Text Generation/Transformation: Easy
- Speech Recognition: Medium
- Optical Flow / Depth Recognition: Hard
- Actually controlling robots with AI: Nightmare
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#145Follow the HuggingFace Colab notebooks. They are well-written and language-related AIs are a great way to get started because you'll naturally have a feeling for what it should produce. Afterwards, do a statistics class. Most algorithms these days are based on softmax, meaning the cross-entropy between two discrete/continuous probability distributions. There's a lot of choice in which distribution to use to model wha…
> to train a state of the art ASR AI, you need roughly 100x A100 for a month, 100,000+ hours of audio recordings, and math knowledge to find a maximum likelihood path through a logit matrix. Do you think there is a distinction between the kinds of problems that take some kind of "raw" signal data (audio, images etc) as input, where deep learning approaches appear to be fruitfully applied, and other kinds of problems…
I agree with you :) Most business-style "automate my Excel" problems can be solved pretty well with regular statistical models, meaning you don't need AI to solve them.
So my impression is that fast.ai teaches you how to use AI methods, but with example problems that didn't need AI to be solved well.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#146My 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…
Can you recommend a good basic stats/probability course? The last one I took was roughly in 1997 ;)
MIT 6.041[1] is a very good course I can recommend. Not sure if MITx 6.431x on edx is the same, but it's the same teacher in any case.
[1] https://www.youtube.com/watch?v=j9WZyLZCBzs&list=PLUl4u3cNGP...
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#147My 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…
Can you recommend a good basic stats/probability course? The last one I took was roughly in 1997 ;)
Richard McElreath's Statistical rethinking is an absolute masterpiece. https://xcelab.net/rm/statistical-rethinking/ on statistics
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#148FastAI. Specifically "Deep Learning for Coders" which was recently updated. https://course.fast.ai/ Do what the instructor recommends: watch each lesson once in its entirety and then re-watch it while playing along. But don't just type their commands verbatim. Try and do something slightly different.
This seems like a really interesting way to do a crash course in do-it-yourself AI—approach it top down and increase context as you go on.
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#149I 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 o…
I think of ML eng as more infrastructure and scalability. Possibly doing tasks like converting lab models into models that can be run at production scale. There is a blurry line between the two because it makes sense for some tasks to have shared ownership - just like you tend to have with people reaching across the stack to get something done in front-end vs. back-end web roles. As with anything, as you get more experience you get more comfortable jumping around and maintaining a larger set of concerns
Re: Ask HN: In 2022, what is the proper way to get into machine/deep learning?
#150Earlier quoted context omitted.
part two of the course covers building everything from scratch and covers almost all of what OP wants, so this is a very good choice. anyone interested in a study group?
i am currently doing the 2019 part 2 of the course......a study group would be amazing
please email me at its.shrey.arora at g m a i l