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

539 points by newsoul 4y ago

By getting into machine or deep learning I mean building upto a stage to do ML/DL research. Applied research or core theory of ML/DL research. Ofcourse, the path to both will quite different. Standing in 2022, what are the best resources for a CS student/decent programmer to get into the field of ML and DL on their own. Resources can be both books or public courses. The target ability: 1. To understand the theory behind the algorithms 2. To implement an algorithm on a dataset of choice. (Data cleaning and management should also be learned) 3. Read research publications and try to implement them.

mlubbad 4y ago
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peterhung 4y ago
If you want to quickly review fundamental stats, you can read: ThinkStats book. After that, I'd recommend: Statistical models: Theory & Pratice by Freedman.
pemaled 4y ago
try to join all these competition because it will keep you busy
gravelc 4y ago
Fastai. Updated series of lectures and notebooks for 2022. High level as well as building neural nets from scratch. Doing it at the moment and enjoying. Good as a starting point for more in depth studies.
- shreyshnaccount 4y ago
  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?
  - istinetz 4y ago
    yes! Let's do it
    - shreyshnaccount 4y ago
      please email me at its.shrey.arora at g m a i l
  - umangkaushik 4y ago
    i am currently doing the 2019 part 2 of the course......a study group would be amazing
    - shreyshnaccount 4y ago
      please email me at its.shrey.arora at g m a i l
- methusala8 4y ago
  Is there any benefit in learning how to code the algo from scratch? In most cases, We will just use standard libraries, unless the goal is to build up intution?
  - tluyben2 4y ago
    I think it's still helpful as it indeed will give you more understanding. You wouldn't want to go for optimised algo's etc unless that rocks your boat, but just basic implementations is good to learn the basics from imho. And pretty satisfying to boot, again, imho.
deleted 4y ago
[deleted]
lexandstuff 4y ago
Fast.ai [1] would still be my course of choice, especially for a strong programmer. After completing that, I think Kaggle competitions are a great way to master your skills. [1] https://course.fast.ai https://course.fast.ai
machinekob 4y ago
+1 for fast.ai it is and was probably best course for noobs for years (i rember 2017/18 courses as big help in DL even that i got exp with machine learning before that [about 1 year of mostly classical ML] :P)
fxtentacle 4y ago
Follow 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 what and it will have strong effects on your gradients and, hence, training trajectory. Concepts like shannon information and entropy are also very helpful for you to monitor training progress. Typical loss values will do exponential annealing and it'll be difficult to see further progress. But if you still reduce the bits of entropy in your classifier, learning is still going well. So you need to understand what to visualize and how to calculate that. As for implementing research publications, maybe start with easy mode and go to paperswithcode.com . There, you will find papers AND their source code, so that you can look at how others implemented their paper. As for FastAI and Kaggle, my personal impression is that it's mostly for toy problems. No real AI researcher would be willing to disclose their full source code to an international megacorp like H&M for a measly $15k in price money, yet similar terms appear to be the default on Kaggle: https://www.kaggle.com/competitions/h-and-m-personalized-fashion-recommendations/overview/prizes https://www.kaggle.com/competitions/h-and-m-personalized-fas... https://www.kaggle.com/competitions/dfl-bundesliga-data-shootout/overview/prizes https://www.kaggle.com/competitions/dfl-bundesliga-data-shoo... https://www.kaggle.com/competitions/feedback-prize-effectiveness/overview/prizes https://www.kaggle.com/competitions/feedback-prize-effective... EDIT: Also, I strongly disagree with course.fast.ai on these points: "Myth (don’t need): Lots of math, Lots of data, Lots of expensive computers" 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. Unless, of course, you're only working on toy problems.
- inciampati 4y ago
  Great suggestions and perspective! Thanks!
- shoo 4y ago
  > 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 that appear in many places in business and the public sector where the input space is not some kind of raw signal data but instead tabular data. I have heard some people argue that the latter kinds of tabular-data style problems can be effectively tackled with a variety of statistical methods, and that deep learning style approaches do not offer an advantage. Maybe fast.ai is commenting on the latter class of problem.
jamesdhutton 4y ago
Andrew Ng's machine learning course on Coursera is a good introduction to the theory. https://www.coursera.org/learn/machine-learning https://www.coursera.org/learn/machine-learning
- peter_retief 4y ago
  I was about to share the same link, really good solid foundation and understanding of what is behind ML
- WASDx 4y ago
  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-h9vYZkQkYNWcItqhlRJLN https://www.youtube.com/playlist?list=PLLssT5z_DsK-h9vYZkQkY... I've watched every video in that playlist. He is a fantastic teacher.
bluelightning2k 4y ago
FastAI. Specifically "Deep Learning for Coders" which was recently updated. https://course.fast.ai/ 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.
- cptcobalt 4y ago
  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.
- carom 4y ago
  For the bottom up side of this, https://deeplearning.ai https://deeplearning.ai. Where fast.ai gets you started training models immediately, deeplearning.ai has you implementing neural nets from scratch in NumPy then transitions to TensorFlow.
  - unoti 4y ago
    This is the best way to get into deep learning: Do both FastAi, and also do Deeplearning.ai. They take opposite approaches that compliment each other perfectly. Fastai starts with the practical and doubles back to help you understand the technical. So in lesson 1 of fastai you're doing practical work, and half way through the course you start learning about taking derivatives of loss functions. Deeplearning.ai on the other hand in lesson 1 you're learning about taking derivatives of loss functions, and you do practical things with it starting about half way through the course. They both are absolutely fabulous for their own reasons, but the two together really cover each other's strengths and weaknesses in an amazing way.
jack_riminton 4y ago
Fast.ai
nonrandomstring 4y ago
You could do worse than go back and make sure your foundation maths is solid. Revise some Discrete Maths books and understand identification, classification, sets, equivalence... Make sure you've a solid ground on concepts like dimensions, functions, differentiation, integration, extrapolation, interpolation, then toughen up your Linear Algebra, optimisation, solving, regression, before getting into approximation and gradient descent. Sure, most of this sounds as dull as a broken clock, but in my observation it makes the difference between students who can just use machine learning tools by copying textbook cases and adopting a lot of fancy new terminology, and those that understand what they're doing. That difference really kicks in once you get off the beaten track of popular use-cases, into applying ML to new, unproven applications. Then you need a deeper understanding of why some algorithms may be useful and others are inappropriate.
- pbowyer 4y ago
  > You could do worse than go back and make sure your foundation maths is solid. This. Though I have no textbook I'd recommend; all of the ones I used were a very hard slog to read, let alone grasp the maths in them.
jstx1 4y ago
Have you taken undergraduate-level linear algebra, multivariable calculus and probability? Those are the prerequisites if you want to approach things a bit more rigorously. If you've covered them, get something specific to ML. I like Hands-On ML... by Geron as a decent intro to ML book. FastAI seems a bit overrated to me - I didn't like that it uses its own helper library or the teaching style but it obviously works for other people. Then there's more exhaustive books on theory - Elements of Statistical Learning, Pattern Recognition and Machine Learning, Bayesian Reasoning and Machine Learning, Murphy's books on probabilistic ML etc. But obviously the theory books have a lot of overlap with each other so there will be lots of material to skip after you've read one or two of them.
samuell 4y ago
There are already good recommendations here for getting into the "standard practice" of ML. To really understand what is going on though, the path I am having some early success with (as a long time developer / data pipeline guy, but newly into the standard python / ML practice) is to run through Kochenderfer's "Algorithms for optimization" from 2019 (MIT press), including implementing the exercises, as optimization is the cornerstone of the majority of ML methods. Some of the most fun I've had in a long time. Freely available here: https://algorithmsbook.com/optimization/ https://algorithmsbook.com/optimization/ From there on, I'm less sure, but expect I might experiment with implementing my own deep learning methods just for fun, or similar.
codethief 4y ago
There is the "high bias low-variance introduction to Machine Learning [for physicists]" -> https://news.ycombinator.com/item?id=17772211 https://news.ycombinator.com/item?id=17772211 Quotes from the comments: > For all my hacker news peeps that wants to learn ML and/or DL, you need to drop everything right now, go print this on the office printer, and sit outside with coffee for the next two weeks and read through this entire thing. Turn off the computer and phone. Stop checking HN for two weeks. Trust me, nothing better than this will come around on HN anytime soon. > The authors are wrong to label this book as useful only to people with a physics background, and in fact it will be useful for everyone who wants to learn modern ML.
obviyus 4y ago
It might not be exactly what you're looking for but I stumbled upon this video by Sebastian Lague the other day: https://youtu.be/hfMk-kjRv4c https://youtu.be/hfMk-kjRv4c As someone who's only ever dabbled into minimaxir's GPT-2 packages this was an extremely approachable exploration (and explanation) of how a neural network works. I can't recommend it enough.
santiagobasulto 4y ago
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 their pipelines, etc. If you get into Data/ML engineer, you’ll be working with these people and learning from them. It’s a longer route for sure, but I think it can yield the highest success rate.
- code51 4y ago
  The trouble is that these positions try to filter you out asking for the same concepts in interviews. Nobody cares for how good a programmer you are. Data engineer positions don't work well if you want to touch cutting-edge concepts like big-scale DNNs. Sure you'd learn about deploying things but if your day-to-day job is not about e.g. ONNX, pytorch, JAX and massive scale training/inference pipelines, then you'd still be left out. Chicken and egg problem. Probably the optimal way is just aiming for top ML research teams right from the start as a new grad. No other way is better.
- jstx1 4y ago
  Don't group data engineer and ML engineer together - they're very different positions. Data engineers typically don't do any ML (in fact teams often create DE positions to differentiate between the people who do ML and the people who don't); for ML engineers is varies depending on the team/company - some work more like applied data scientists, others focus almost entirely on infrastructure and deployment.
- wokwokwok 4y ago
  > 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 “productionise” them. - cleaning up behind brain dead scheduling systems that fail unexpectedly. - constant churn with partners and cloud products for whatever the latest hotness is. If you want to be solving actual problems with ML, this is a dead end. It’s IT support for the people doing real work. …so, it depends on your goals. Getting a job? Sure! Everyone wants a workhorse who they can dump all the annoying problematic on-call tasks to. Learning ML, contributing to research, building models? This isn’t a path that leads there.
k_sze 4y ago
If you want a good theoretical foundation based on maths, Caltech's Learning From Data course is good: https://work.caltech.edu/telecourse https://work.caltech.edu/telecourse You need to be not afraid of doing proofs of theorems (most of them have to do with stats because machine learning is basically stats on steroids).
brw95 4y ago
As always, it depends. I started out without *any* background knowledge a few years ago. Found the Data Scientist career track of Datacamp pretty helpful, since it goes beyond programming and includes the mathematical and statistical theories as well. (https://www.datacamp.com/tracks/data-scientist-with-python https://www.datacamp.com/tracks/data-scientist-with-python) It's basic, but a solid foundation to build upon. If you're already familiar with most of these topics, fast.ai is the way to go!
throwaway81523 4y ago
Don't know about 2022, but in 2020 the fast.ai video series was a good way to get started. It has been revised a few times since then, so chances are it is still good. If you want something more theoretical there is a book by Hopcroft et al. that was released in draft form a number of years ago. It appears to be out for real now: Foundations of Data Science, by Avrim Blum, John Hopcroft, and Ravindran Kannan. Blurb and video lectures: https://www.microsoft.com/en-us/research/publication/foundations-of-data-science-2/ https://www.microsoft.com/en-us/research/publication/foundat... I just found these so haven't looked at them yet. The book draft (2014, wow) is here: https://www.cs.cornell.edu/jeh/book11April2014.pdf https://www.cs.cornell.edu/jeh/book11April2014.pdf I didn't stick with it long enough to make much progress, unfortunately. Kaggle.ai problems are a good set of practical projects even if you're not aiming to be competitive at them (which takes a lot of effort and resources). The Fast.ai vids are ok as preparation for them.
logicchains 4y ago
I'd highly recommend reading this book, which is available free online: https://www.deeplearningbook.org/ https://www.deeplearningbook.org/ . It covers the foundations up to 2016 very well, with useful references for understanding the underlying math/theory.
dejv 4y ago
A lot of people here recommend fast.ai which is solid, but not really that useful if you want to do research. I would start with math foundations: basic linear algebra, stats, probability and some analysis. CS undergrad level is plenty of math for start. Then I would try to understand back prop on intimate level: learn how to calculate gradients, maybe take a look on how autograd works as well. Then you should know a bit to pick your next steps by yourself.
thrown_22 4y ago
Learning how to do machine learning and learning how to clear data is a bit like asking what the best way to become an award winning author with very neat penmanship is. One has nothing to do with the other. That said data engineering at scale pays a lot more than deep learning but is also a lot less fun. Figure out which you'd rather do.
phonebucket 4y ago
> Read research publications and try to implement them. In my opinion, jump straight into this! Learn prerequisites as you need them. I found Goodfellow's book [1] to be helpful to learn some basics. But don't think you need to read the whole book before you start reading and implementing research papers. If you try and build up all the fundamentals thorougly, you run the risk of going down a very deep rabbit hole e.g. learning real analysis so you can learn measure theory so you can learn measure theoretic probability theory so you can learn stats properly etc. You can be a productive researcher and patch up the fundamentals over time. [1] https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
f1shy 4y ago
Read “AI a modern approach“ and do the MIT and Stanford courses that are available in Youtube. Then you can go deeper into the branches presented in the book and courses. The problem I’m seeing now, ist that everybody seems to think AI/ML is NN. Nothing further from truth!
urthor 4y ago
1. Clarify your goal. Do you want to: a) Become an academic in mathematics/statistics. b) Become an academic in computer science with a focus on artificial intelligence. c) Become a MLE in "regular" statistical applications. Aka bayesian classification, "core" statistical principles. d) Become a specialized computer vision/natural language processing focused MLE. e) Become a generalist software engineer who can whip out the above if needed. In no way is e) the inferior option. Generalists who can write code fast with 100% test coverage and pristine logging are by far the segment the industry has the shortest supply of. There are TONS of math guys. Vanishingly few Principal Engineers who can write a design document and lead a project. (Machine learning customers are OBSESSED with test coverage and verifiability. Believe it or not, multinational corporations generally don't want to unleash a {your_adjective_here}ist algorithm on the world.) 2. Study the above, properly. To study the math, Elements of Statistical Learning/Algorithms by Goodfellow. Start on page 1, do every second exercise. Publish a summary of every chapter you finish with your answers to GitHub. 3. Pursue your goal in a publicly verifiable manner. See: https://news.ycombinator.com/item?id=32071137 https://news.ycombinator.com/item?id=32071137
- ralphmelish 4y ago
  The Elements of Statistical Learning is by Hastie et al, not by Goodfellow. Goodfellow wrote Deep Learning. They are both available for free on their websites.
  - sitkack 4y ago
    https://hastie.su.domains/ElemStatLearn/ https://hastie.su.domains/ElemStatLearn/ https://www.deeplearningbook.org/ https://www.deeplearningbook.org/
alanfranz 4y ago
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 good suggestions on this thread, I won't chime in), and you miss the fact that, sometimes, a simple decision tree, or dimensionality reduction approaches (e.g. PCA or ICA) can yield an incredible value in a very short time on huge datasets. I had written a fairly short post about it when I finished my georgia tech path https://www.franzoni.eu/machine-learning-a-sound-primer/ https://www.franzoni.eu/machine-learning-a-sound-primer/ 3) It can take a lot of time to become effective in ML, effective as in, what you _manually create_ is as effective as picking an existing trained model, fine tune it, and use it. This can be frustrating: low hanging fruits are pretty powerful and you don't need to understand a lot about ML algorithms to pick them up. 4) Consider MOOCs or online classes. I took Georgia Tech OMSCS, I can vouch for it and some classes force you to be a data scientist and read papers as well, and you can have "real world" recognition and discuss with your peers, which is useful!
- nkzd 4y ago
  I am a CS major but I was always bad at math. Can you recommend your favorite resources for learning probability and statistics basics?
  - alanfranz 4y ago
    “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.
  - poulsbohemian 4y ago
    I was a CS major who made a 20 year career in software who was always told I was bad at math and struggled all through school with math. Somewhere about ten years into my career I began to realize that it wasn't really that I was bad with math, but that the way math is taught just doesn't work for most people. And, a lot of that math that you were expected to learn is really only directly applicable in very specific circumstances that you might not encounter in your career - which is not to say that the mental exercise of learning them weren't worthwhile! So my point is, if it isn't making sense the way you are being taught, go explore other avenues. There's no way these machine learning algorithms would have made sense to me as a 20-something undergrad, but as a 40-something who can explore them via software rather than a whiteboard, they really aren't that complicated to get started.
elisbce 4y ago
The dilemma is that although there are recommendations like fast.ai to get your hands dirty quickly into ML/DL, none of the good AI researchers and practitioners got there via these quick tutorials. They got there via rigorous linear algebra, traditional ML, statistics and related computer science knowledge. I would say try fast.ai for a quick taste of what ML/DL is like, and then go back to linear algebra, deep learning and stats courses from top schools while picking a personal project goal to achieve (e.g. reproducing a popular CVPR/ICML paper results or building your own XXX) Once you go through a full lifecycle of building something from scratch, you will have a much better understanding about where you are and wanna go from there.
roschdal 4y ago
Why would you possibly want to do such a thing towards yourself?
NalNezumi 4y ago
I can only talk from my own experience but there's other way than just straight up go to ML/DL and crank out new models. That way is to come from the application domain side. [0] For me that was robotics, with the motivation that traditional method felt like it wouldn't scale outside static environments so I started to look in to ML/DL (Deep Reinforcement Learning really) and from the looks of it I'm not alone. [0] Now I do research in it, without a PhD nor taking any courses in it at my masters. (except one DL course where we had to code everything including gradient flow from scratch. No framework) Frankly, going pure DL at research level today seems like a steep uphill; the top labs and research institute(including industries) are the ones that are producing (and notably training) most of the SOTA models. Getting in to those circles are your best bet but then a PhD at a top university under a top professor is the best bet, and competition to get in to those are insane [0] https://www.natolambert.com/writing/path-into-ai https://www.natolambert.com/writing/path-into-ai
- sitkack 4y ago
  I gave a more poorly expressed version of what you said. With one change, I think the author has a domain in mind or plan. They really asked how to get to > Read research publications and try to implement them. They might want to be able to use the body of AI and working code to apply it to a different but specific problem. Be a power user of existing models. But totally agree and well said!
mitchellgoffpc 4y ago
https://www.deeplearningbook.org/ https://www.deeplearningbook.org/ and http://incompleteideas.net/book/the-book-2nd.html http://incompleteideas.net/book/the-book-2nd.html are excellent resources for supervised and reinforcement learning, respectively, and some knowledge of statistics and probability go a long way. But I think by far the most important thing is to just start training models, even very small ones, and developing an intuition for what works and what the failure modes are. - Get really comfortable with matplotlib or your graphing library of choice. Plot your data in every way you can think of. Plot your models' outputs, find which samples they do best and worst on. - Play around with different hyperparameters and data augmentation strategies and see how they affect training. - Try implementing backprop by hand -- understanding the backward pass of the different layers is extremely helpful when debugging. I found Karpathy's CS231n lectures to be a great starting point for this. - Eventually, you'll want to start reading papers. The seminal papers (alexnet, resnet, attention is all you need, etc) are a good place to start. I found https://www.youtube.com/c/YannicKilcher https://www.youtube.com/c/YannicKilcher (especially the early videos) to be a very useful companion resource for this. - Once you've read some papers and feel comfortable with the format, you'll want to try implementing something. Important tricks are often hidden away in the appendices, read them carefully! - And above all, remember that machine learning is a dark art -- when your dataloader has a bug in its shuffling logic, or when your tensor shapes get broadcast incorrectly, your code often won't throw an error, your model will just be slightly worse and you'll never notice. Because of this, 90% of being a good ML researcher/engineer is writing tests and knowing how to track down bugs. http://karpathy.github.io/2019/04/25/recipe/ http://karpathy.github.io/2019/04/25/recipe/ perfectly summarizes my feelings on this.
- jstx1 4y ago
  Goodfellow's book is a bad recommendation for people don't already know the material in it.
- randcraw 4y ago
  I second Karpathy's version of cs231n (2016). He's an amazing lecturer. A good alternative to Goodfellow is "Dive into Deep Learning" (https://d2l.ai https://d2l.ai), which is free and more up-to-date, interactive, and practical, IMO. Videos of a 2019 Berkeley course based on it are available too (https://courses.d2l.ai/berkeley-stat-157/ https://courses.d2l.ai/berkeley-stat-157/).
m3at 4y ago
There's so much resources it's hard to pick… If you're already studying CS and like to learn by building, I'd recommend to do that for ML too! Pick a real problem, try to build a ML solution for it and while doing so keep a list of things you'd like to dig deeper into. Then go back to that list and pick one item to study, and iterate. Happy to have a chat and give you specific pointers if you'd like (email in profile), I got my master in ML in 2016 and applied it in the industry since.
yodsanklai 4y ago
If you want to do research, I'd recommend an academic path. Get an MS from a reputable university. If you can't do that, try to follow their syllabus on your own (textbooks, articles). MOOC are a starting point, they often offer classes with little pre-requisites, but that'll only get you so far.
teruakohatu 4y ago
I suggest first understanding machine learning in general before jumping into deep learning. The book ISLR2 is very accessible and starts with linear regression and works through many other methods including neural networks. There is a Edx course based on the book. https://www.statlearning.com/ https://www.statlearning.com/ https://www.edx.org/course/statistical-learning https://www.edx.org/course/statistical-learning
schiptsovgithub 4y ago
There is a early recording of Andrew Ng Stanford course before he became the star of the field. It is mostly math behind ML. There are also his lecture notes from 2014 I suppose. It is really all one need to know. Machine learning or Deep learning is just pattern recognition which uses NNs as the basis for representation foe the "matcher". The real problem of ML is to find someone who would pay you for this because we are in a bubble. It is math and algorithms that matters and there are just a few of them. All the tooling could be mastered in a month.
alex-gdv 4y ago
imo the best way to learn is to jump into a project. if you want some basic intuition on neural networks, 3blue1brown has some great videos. for everything else, just google things and you'll find lots of resources. medium and towardsdatascience articles have saved my life so many times while working on ML/DL uni projetcs. if you're looking to play around with current research, use https://paperswithcode.com https://paperswithcode.com.
wanderingmind 4y ago
If you are looking for machine learning outside of Deep Learning, there are just 2 books 1. Elements of Statistical Learning (very frequentist treatment) by Hastie et.all [1] 2. Pattern Recognition and Machine Learning by Bishop(for a Bayesian treatment)[2] Both are freely available online. Reading one book will get you to top 5% practitioners and reading both will get you to top 1% [1] https://hastie.su.domains/Papers/ESLII.pdf https://hastie.su.domains/Papers/ESLII.pdf [2] https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf https://www.microsoft.com/en-us/research/uploads/prod/2006/0...
- plonk 4y ago
  Machine Learning: a Probabilistic Perspective by Murphy may be a better reference. Murphy has more up-to-date books. > Both are freely available online. Reading one book will get you to top 5% practitioners and reading both will get you to top 1% At which percentage do you start meeting math PhDs from top schools? You'll most probably never meet their level of understanding just by reading books or doing exercises. Having read Bishop, I don't have one tenth of the knowledge I'd need to do research at that level, you need more exposure than that.
  - sitkack 4y ago
    https://probml.github.io/pml-book/ https://probml.github.io/pml-book/
- sitkack 4y ago
  That [1] is a link to the uncorrected 2009 copy of ESL. The main page provides links to the most up to date version. https://hastie.su.domains/ElemStatLearn/ https://hastie.su.domains/ElemStatLearn/
pfarrell 4y ago
There was a thread a few days which has some recent comments on this topic,”How do you break into a career in machine learning? (2020)” [0] 0: https://news.ycombinator.com/item?id=32342925 https://news.ycombinator.com/item?id=32342925
frontman1988 4y ago
It's probably a bit too late to get into ML. It's oversaturated with a lot of wannabe "Machine Learning enthusiasts" If you still want to get into the field a masters/phd is a much safer way to get proper ML jobs and then prosper in them.
- tdeck 4y ago
  Not sure why this is downvoted, perhaps because it's condescending. But I've observed the same thing. The data science job market is not that great right now; It's tough even if you have a master's degree.
sitkack 4y ago
I am going to give you some meta commentary. > ML/DL research I think you should apply ML deeply to a domain you care about, but see if you can find a domain that can be both generative as well as for understanding. If you are heavy into the math and don't need a grounding basis, maybe you don't need a domain to apply the ML research to, but the best scientists had a problem they were trying to solve, not just "doing research". Basically research in strong direction, for strong purpose solving a problem. I guessed you asked a low level mechanical question. How do I get from A to B. You might already have the domain. So to answer the actual question, I'd pick something like MNIST (digit recognition problem) and master it by hand from scratch using multiple techniques, as many techniques as I could find. So that I am applying each algorithm to a fixed problem, so that the algorithm and then later a paper the algorithm gets embedded in my mind. Use only cleaned datasets, spend zero energy on those a the beginning. Cleaning is a separate job and two different things don't need to be learned here. In fact stick with only industry benchmark data so you can compare your results to more papers.
dr-neptune 4y ago
> By getting into machine or deep learning I mean building upto a stage to do ML/DL research. > The target ability: > 1. To understand the theory behind the algorithms > 2. To implement an algorithm on a dataset of choice. (Data cleaning and management should also be learned) > 3. Read research publications and try to implement them. There are many different ways that people do ML/DL research these days. Some people do more theory-work which will necessarily be more focused on mathematics, and others do more of an applied approach which will be more focused on coding and iterating. For theory-driven work, I think Michael I Jordans list is still pretty solid: > https://news.ycombinator.com/item?id=1055389 https://news.ycombinator.com/item?id=1055389 I would focus on the fundamentals first though: 1. get a solid background in mathematics - analysis (a suggestion is Baby Rudin) - probability (Grimmet and Stirzaker, maybe something with measure theory after) - statistics (Casella and Berger or Wasserman's book is a good start) 2. get a solid foundation in statistical machine learning - Introduction to Statistical Learning is a fantastic start - Then choose 1 or both of the following: - Elements of Statistical Learning for a Frequentist Approach - Pattern Recognition & Machine Learning for a Bayesian Approach 3. get a baseline understanding of deep learning - the deep learning book by Goodfellow is decent - start reading papers here and trying to implement them If you get through to this last step, you are probably solid enough to get a job building models. If that's the route you want, then begin iterating on learning about new approaches in papers (look for papers with code / data) and implementing them. If you want to go the academic route, you have enough of a view of the field to begin specializing further. Choose a sub-domain and dig deep if you want to do more deep learning work. Maybe revisit Michael I Jordan's list if you're still confused about where to go. A lot of those books will feel a lot more familiar. Best of luck!
qwertyuiop12 4y ago
The Machine Learning course by Andrew Ng in Edx. A basic ( and not at all! ) approach to know if it is funny for you
maurits 4y ago
Stanford CS231n: Convolutional Neural Networks for Visual Recognition [1] The assignments are excellent and will let you implement a deephish network from practically scratch, before diving into modern frameworks and applications. This is not an instant-gratification with fancy results kind of course. But put in the work, and you will learn some very cool stuff. [1]: http://cs231n.stanford.edu/index.html http://cs231n.stanford.edu/index.html
- schizo89 4y ago
  This is the best intro route. I 2nd this
meltyness 4y ago
Describe the new calculus that leads to the design of multihead attention units, identify 3-4 other useful novel units using your pattern, propose hardware that can perform those operations with greater speed or efficiency.
bjourne 4y ago
That's a multi-year process. Being proficient with TensorFlow and PyTorch is not sufficient to do useful research. I suggest you begin by implementing a neural network from scratch in your favorite programming language. Writing up the code for matrix multiplication, dot product, back propagation, etc. will teach you a lot.
aqsalose 4y ago
To piggyback on the OPs question, I for one think the part in parenthesis is actually most important: >(Data cleaning and management should also be learned) There are many students and graduates who either didn't want to do research in the first place or didn't get that research grant or position and looking to get employed in private sector with their degree. Many universities and colleges have now also retooled some of their statistics degrees as dedicated "data science" curriculum who either know basics of ML/DL or have the prerequisite background to learn quickly. However, in my experience (I am extrapolating from my own past job search experiences) while "understanding theory behind the algorithms" counts still for something, it is much less than one would think. Familiarity with the software technologies and practical implementation is what counts much more. This includes not only "data management", a phrase which makes it sound like the data simply exists somewhere and only needs to be managed (not unlike a Kaggle competition), but also the data pipeline management from generation/collection to analysis and communication of the results, and deploying the software the implements it all, and so on. I suppose (never been on that end of the interview table) given any two candidates to interview, it is very difficult to evaluate how deeply one understands theory of some algorithm compared to other if they both demonstrate some basic understanding (and what is the practical use of possible difference in insight from such differential, anyway?). Likewise, I assume it is somewhat easier to gauge whether someone seems to able start delivering results or contributing to their on-going work quickly if they have the relevant technical skills and/or domain knowledge.
tzury 4y ago
Start here https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCkfOxvzCqGEFzWBy4e9Uedd9k/edit https://docs.google.com/presentation/d/1kSuQyW5DTnkVaZEjGYCk... Then move here https://developers.google.com/machine-learning/crash-course/ml-intro https://developers.google.com/machine-learning/crash-course/...
Kalanos 4y ago
Read this: https://docs.aiqc.io/pages/explainer.html https://docs.aiqc.io/pages/explainer.html Practice these: https://docs.aiqc.io/pages/gallery.html https://docs.aiqc.io/pages/gallery.html
mccorrinall 4y ago
Read this book: https://www.oreilly.com/library/view/hands-on-machine-learning/9781491962282/ https://www.oreilly.com/library/view/hands-on-machine-learni...
dmc-au 4y ago
1. Watch the 3blue1brown neural network series for a gentle refresher on the underlying maths and the big picture of neural networks (and to be inspired): https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_67000Dx_ZCJB-3pi https://www.youtube.com/playlist?list=PLZHQObOWTQDNU6R1_6700... 2. Run through the catalogue of StatQuest videos for topics of interest in machine learning etc. This includes step-by-step maths and code explainers: https://www.youtube.com/c/joshstarmer/playlists https://www.youtube.com/c/joshstarmer/playlists 3. Watch more 3blue1brown videos if you need to step further back to refresh on calculus and linear algebra (that's most of the maths you'll need). If you're hooked and can't get enough of the above content, then congratulations and welcome to the Matrix.
ravish0007 4y ago
Off-topic but Norvig's talk (As We May Program) intersection of hacking-skills + statistical-thinking + domain-knowledge (https://vimeo.com/215418110 https://vimeo.com/215418110)
jokoon 4y ago
I recently asked reddit how to label images by downloading a pre trained network that used image net, I got no answers. I don't know how long it would take to train such network with a cheap laptop. There are tutorials, but I don't see any cookie cutter thing. I thought there would be demos for this, since image labeling is an old problem.
- ummonk 4y ago
  FastAI has ready-to-run code that does just this. They seem to have an ImageNet package https://github.com/fastai/imagenette https://github.com/fastai/imagenette
  - jokoon 4y ago
    this link seems to be a ML competition, not trained datasets.
bo1024 4y ago
> I mean building upto a stage to do ML/DL research. Applied research or core theory of ML/DL research. The vast majority of people who do this have graduate degrees. I'm biased, but I think getting a graduate degree in the subject would be the default suggestion. Are you considering it?
ford 4y ago
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 academic research experience. Few companies are big enough to support true research, and the ones that are have a very high bar even for people with PHDs What I call "data scientists" apply math/ML to real problems. The people I see here have a quantitative background like physics/math/CS. Often they have more general quantitative skills that go beyond ML. People like this will might work on things like fraud where an eng pipeline exists and small improvements in the model are valuable. There are more of these roles than "true research" and they exist at small companies because it's applied. You can get into this with demonstrated evidence in side projects + a convincing background, but professional education might be the most sure way. Finally - there's a lot of demand for engineers who can do both modeling and the requisite engineering. A model is a small part of what goes into a production ML feature - you need a data pipeline, automated retraining/prediction, a place to deploy the model, monitoring on eng stats + data stats, and the usual application backend/frontend to do something with the results. You might be able to get into this with some demonstrated experience in side projects assuming you're a SWE already, and depending on your standards for where you want to work.
- freedomben 4y ago
  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.
  - jb3689 4y ago
    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
mhb 4y ago
A nice mix of application and background: https://pyimagesearch.com/ https://pyimagesearch.com/
data_maan 4y ago
1. The theory by now is filling bookshelves, so forget learning about "the theory". Learn the foundation (to see where you're niche --e.g. optimization for fully connected NNs; last I heard it should be done by second-order methods if you have the computing power-- fits in) and then learn the niche's theory. This will require at least upper undergraduate level math BTW. 2. You could get by knowing the theory in a handwavy way. Not ideal but I've seen people do it. For implementation that is enough in many case. 3. Again, "research" is too general. While you might understand some experimental ICML papers, it's very unlikely you will understand a single COLT paper if you don't know a lot of math.
jll29 4y ago
> 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.
asellke 4y ago
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-practitioners/ai-all-humans-course-delight-and-inspire https://cloud.google.com/blog/topics/developers-practitioner...
sabr 4y ago
On a related note, to get into ML/DL you'll have to read a lot of arXiv papers. I built Smort.io to easily annotate and collaborate on arXiv papers. Just add smort.io/ before any arXiv URL to read it in Smort. Demo: https://smort.io/demo/home https://smort.io/demo/home
adamsmith143 4y ago
If you really want to do research then no question you need to go to grad school for at minimum an MS. If you are still a student that means getting in touch with ML professors at your school and trying to get published before you graduate. Top ML programs in the US are extremely competitive and you likely don't stand a shot of getting in without a few NeurIPS/ICML/CVPR papers. If you just want to work as an ML Engineer then take as many courses as you can on the subject before you graduate and get internships/apply to jobs. Nothing special here.
tunesmith 4y ago
I've thought about going in this direction multiple times, but I have this impression that it's pretty much unavoidable that you'd be working with annoying ETL processes and "cleaning data" (parsing, transforming from one set of columns to another, etc) over and over again. Is this true? I hate that stuff, but I'm particularly interested in graphical data structures and I even like probability and statistics and linear algebra, so I've been torn.
- kingcai 4y ago
  It's an unavoidable part of the job, but it's definitely not most of the work. I feel it's kind of like writing tests in SWE - something you do because it's beneficial even if it's not enjoyable.
kingcai 4y ago
My best advice is to find people who do what you want to do and try to learn as much as possible from them. If you're interested in doing ML/DL research I think the best way to get into the field is to reach out to professors. I studied ML/DL (books, projects, classes, reimplementing papers) for several years in undergrad, but discussing and debating ideas is the one thing that took my understanding to a much deeper level. A good professor will also point out gaps in your knowledge that you might be missing. A second bit of advice: Programming (and execution) skills are IMO heavily undervalued by people looking to get into ML. The faster you can write code, debug, and implement new things, the easier it is to produce good research. Some books I liked: PR & ML (Bishop), Deep Learning Book (Goodfellow), AI: A Modern Approach (Norving), Elements of Statistical Learning (Friedman)

bitL 4y ago

Data Engineer is the outsourced part of what no ML researcher wants to do - a thankless, high-pressure, dead-end job which in no way leads to actually doing ML later - it would pigeon-hole the OP as unfit for real ML. The best way is to take Stanford Deep Learning courses at SCPD, build a reputation, do real ML work (even if it's not a PhD, it's the same courses Stanford PhDs take).

LibrandLefticle 4y ago
I'm struggling to find a single resource that ELI5 step-by-step how a neural network does digit classification (learns 0-9) in a grid of 3x5, like the traffic signal countdown timer
psi75 4y ago
I agree with your first sentence. I'm not sure I would recommend SCPD. If you want to do real ML work, you pretty much need the PhD. This is a hard thing for people who have 140+ IQs but do poorly for whatever reason with formal education to accept, but it's true. Even if you get one real ML job without a doctoral degree, you won't get a second one. Sure, other 140+ IQs can recognize very smart people with only (or not even) a bachelor's degree, but (a) your career in industry will be influenced by the opinions of not-smart but politically empowered people who rely on heuristics like educational prestige because they can't judge the genuine article, and (b) some of those 140+ are nevertheless scumbags and will use (a) against you. If you want to be a serious player in an academic field like ML, you need to not only get the degree but start publishing and never stop. It doesn't matter all that much if your papers are any good; no one in industry will ever read them. But you need the image of a successful academic who's just slumming it and can go back any time.
- bitL 4y ago
  So basically no chance for the OP to ever get to ML as getting into a top 10 ML school for a PhD is a minor miracle, finishing it even bigger and that's just the initial qualification step?
  - psi75 4y ago
    Academia remains an option even if you don't get into a top-10 ML school, if your research is good. Granted, it's tougher to get published and cited, because you're less likely to know people who can push your work, but if you do good work, you can still play. Government may be an option, although it doesn't pay as well as industry and you can end up in a comfortable but stifling role. In corporate, though? Yeah, you pretty much need to have the appearance of star power, which means degree prestige matters. Whether you're actually any good (and, trust me, there are plenty of mediocre people from top schools) doesn't really matter, because the decision-makers are too stupid to know the difference. There are ways to play this, though, if you're aiming at industry. Harvard isn't a top-10 CS department, but the people in corporate aren't going to know that, and so "Harvard PhD" is going to make them fellate you just because it's Harvard. That may be an avenue. Or, better yet, get a PhD in something that sounds technical but is easier and less selective. That said, if your goal is to play the corporate game and make a lot of money, you should probably forget about ML and focus on becoming a manager as quick as possible. If your goal is to do intellectually stimulating work, you should probably not consider corporate, because your work is going to be evaluated by people who are literally 50 IQ points too dumb to do so, and while this noise factor is manipulable rather than truly random, the people who have the skills to do perform said manipulation tend to go into management, not technology.
- depr 4y ago
  I don't think what you consider "real ML work" is what OP is asking for. While they wrote "research", the three points they wrote at the end is not research, and I don't think they want to be an academic and/or write any papers.
- zivkovicp 4y ago
  There is a lot of truth in this comment; it's not pleasant, but it's truth nonetheless.
- time_to_smile 4y ago
  I've worked in industry doing what I would consider "real ML" (i.e. shipping models that are core, revenue generating product features into production) for a decade at a range of companies from startups to fortune 500 companies and the part about needing a PhD is entirely nonsense (as is the majority of the content in this comment). Maybe if you consider doing "real ML" exclusively working with Deep Mind or on Meta core research team, but there's a lot more to "real ML" than just these teams. I'm curious how long you've been in industry and what types of orgs you've worked at to get this impression? edit: In general the comments in this post are bizarrely out of touch with reality and have really shifted my perception of the avg HN commenter.

spywaregorilla 4y ago

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 variable when training a model). Doesn't seem related to stats or probability at all to me. Just critical thinking skills

thegginthesky 4y ago
I prefer to use the approach "learn as you go". There's nothing to block someone from learning some basics, a library like Scikit Learn and then learn by doing examples. But if anyone wants to become an expert into ML/DS, learning statistics and probability is fundamental. Books like A First Course in Probability, Introduction to Statistical Learning and Elements of Statistical Learning, to name a few, are very important. A lot of the mistakes done in practice are based on a lack of understanding in sampling techniques, how statistical metrics can be misleading and so on. First thing I learned in statistics is the difference between quantitative and qualitative information. If someone knows this before hopping into Kaggle, they know that categorical features can't be used as continuous features.
data_maan 4y ago
Depends on your goal. If you want to read/implement things from COLT article a 101 stats/probability won't really cut it. Though for applied papers that's sometimes enough. But heed Larry Wasserman's advice: "Using fancy tools like neural nets, boosting, and support vector machines without understanding basic statistics is like doing brain surgery before knowing how to use a band-aid."
alanfranz 4y ago
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.

listenallyall 4y ago

These types of anecdotes make the actual practice of both ML and AI seem rather, well, less than scientific. There is supposed to be Ph.D. level math behind all of this, yet an amateur with admittedly no ML background is part of the team. In Star Wars, it takes Luke Skywalker years to learn to use a light saber skillfully. Then in The Force Awakens, some ex-Stormtrooper with no training picks up the light saber and within 5 minutes is a pro. Kinda ruined the mystique of Star Wars, just like people jumping into ML with no training ruins the mystique of ML.

jokethrowaway 4y ago
You're disillusioned if you think a PhD is what makes the difference. Smart people will be able to contribute even if they don't have a PhD. Some PhD are useless and everyone is wondering how the hell they go through that.
mkl 4y ago
Is mystique worth having? In fiction, sure, but I think not in R&D. I think a lot of ML and AI isn't especially scientific, but I also think there's a lot of low-hanging fruit in applying it to new areas, and both of those make it easier for an amateur to contribute.
deleted 4y ago
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natch 4y ago
Fuck mystique.
LeetHacks 4y ago
I am very against the idea that somebody with a PhD is the only one that can do a certain kind of work. But I am ofcourse biased given that you call me out. Creative and critical thinking is not exclusive to people with a PhD. The ability to understand ones strengths and weaknesses is not exclusive to a PhD. I would never attempt to write or publish a paper without help of somebody with stronger mathematical or statistical knowledge. On the other hand they should not write source code for a paper without consulting somebody with a strong background in sw engineering. You complement each other. Power is in recognizing that. You would be surprised how many software bugs I have found that invalidated entire (draft) papers. A PhD in ML doesn't save you from that.
Werewolf255 4y ago
I think your metaphor kinda plays against your point. Years and years ago in the movie universe, Luke had to painfully learn to use his lightsaber from a mentor who passed down techniques and philosophy to the student. In the current day of the movies, lightsabers are understood to be powerful, yet temperamental and exotic, weapons. Mildly trained individuals can use them, even if it's to a limited extent (i.e. flick switch; shiny side is the business end; heat bad, ouch, no touch). To belabor the metaphor, you've also had a tradition of people, from all walks of life, using vibroblades (basic to advanced standard statistical analysis and regression) in order to achieve some level of parity against users of lightsabers.
data_maan 4y ago
Let me expound on this, as there's a lot of PhD hate in the comments parallel to mine. What is unique to a PhD is that you took a very long time to master a small slice of the knowledge pie. The emphasis here is on long time: Most people simply aren't willing to go for years on a low salary and tedious job. It doesn't mean PhD's are more (or less) creative, top coders and whatnot; it means we took the time to read all the papers, to know all previous solution attempts and who all the big players in the field are ("all" w.r.t. our niche). It also mean we can read papers much faster than other people because that is basically what we do all day. There you have it! Now don't send me ML job offers, cause I gotta read this next obscure paper to figure out if they are legit. :D Just kidding.
fisf 4y ago
Much 'applied' ML is based on tweaked existing models, getting them production ready and integrating them into a product. That's inherently a bit of non-trivial engineering work, but as you pointed out, not scientific per-se.
rjdagost 4y ago
There is PhD level math involved. And yet, ML (deep learning in particular) is much more of an empirical endeavor than many would like to admit. A deep understanding of the underlying mathematics does not necessarily give you a better model. Modern models are so complicated that no one can reason through them. Parameter spaces are non-convex and fully of ugly pathologies that make neat and tidy analysis methods useless. From one perspective, it is disheartening that a deep understanding of the underlying methods doesn't necessarily win the day. From another, it is quite remarkable that having good implementation skills and a methodical mindset can get you quite far.

mrcartmeneses 4y ago

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?

badpun 4y ago
> I’ve kinda developed the view that large organisations come to mirror the Russian Communist Party. Only the ones which have an unkillable cash cow. So, I suspect Google or large banks are mostly like that, but places like SpaceX or even large consulting firms (Delloitte, IBM etc., where managers essentially eat what they kill) cannot allow themselves to degenerate into a Chinese court.
- bsenftner 4y ago
  Now this is interesting. I've always found it fascinating that when profit is on the table, democracy is nowhere to be found. I've looked, not too hard TBH, for essays and literature discussing the correlations to business model management structures and government/nation political hierarchies - not education level (propaganda), but critical analysis. I've been an employee of several of the top corporations on our planet, and the idea that corruption is not rampant is a farce. One simply lives within the environmental constraints and leaves when it gets to be too much. Does caring about corporate (and the larger realm of ethics) cast one incompatible with a modern corporate hierarchy?
  - psi75 4y ago
    Unfortunately, the only way to prevent hierarchy is to create a limited hierarchy (this is the purpose of constitutions) a priori; hierarchically naive organizations fail on this account. External parties will demand hierarchy simply because they want to know your organization (or nation) isn't wasting their time--no one wants to deliver a sales pitch to people who can't authorize purchases. If they're not careful, a group of people can end up in a state where the necessary-for-external-relations hierarchy becomes a total one. You see this with startup founders; the one who talks to the investors the most ends up in charge, and the ones who deal with employees or low-status counterparties lose power. This is why "flat" organizations can't really work; people who need things from the organization demand to know who to talk to in order to actually get things done, and eventually those "who to talk to" people end up with informal, then formal, power and it's very difficult to get them to give it back. The large-scale failure of democracy that's happening all over the world is something different, though. Regulation is struggling to keep up with technology, and it doesn't help that nation-states have already been doing a piss-poor job of protecting people from their employers. If the US falls in the next 20 years, it won't be due to Covid or Trump or nation-level adversaries; it'll be due to the obscene power given to employers, who can literally ruin an employee's life--not just fire him, but anally ravage him in perpetuity with bad references--for any reason or none. Eventually, unless national governments start dropping serious lead pipe on employers' heads, people are going to tire of paying 30+ percent of their incomes to a government that lets bosses get away with this shit.
    - badpun 4y ago
      > If the US falls in the next 20 years, it won't be due to Covid or Trump or nation-level adversaries; it'll be due to the obscene power given to employers, who can literally ruin an employee's life--not just fire him, but anally ravage him in perpetuity with bad references--for any reason or none. How do you define "US falls"? >Eventually, unless national governments start dropping serious lead pipe on employers' heads, people are going to tire of paying 30+ percent of their incomes to a government that lets bosses get away with this shit. People endured much worse in medieval times, and endure much worse right now in China.
      - bsenftner 4y ago
        > People endured much worse in medieval times, and endure much worse right now in China. Really? The USA has hollowed out portions of the country equal/worse than the worst 3rd world countries.
        badpun 4y ago
        I hope you're hyperbolic, the worst 3rd world countries have no governance (unless you count local warlords), 5 year olds working in dangerous and toxic conditions, hunger and slavery.
        bsenftner 4y ago
        You don't realize what is going on in the United States. We have portions of the USA where the police don't even bother, and are run by local gangs. We also have children working, in dangerous and toxic conditions. We also have hunger, and yes we have slavery: prison labor. The USA is not what you think it is.
        badpun 4y ago
        > We also have children working, in dangerous and toxic conditions. Can you elaborate on that? I've never heard that parcitular thing about the US. For reference, In Kongo, there are 5 year olds today carrying heavy buckets in makeshift cobalt mines, a'la XIX century England or France (plus the toxicity of cobalt, people who work in these mines get cancer if they don't die in an accident first). Even with whole families working in such conditions, the pay is not enough and not stable enough to sustain the family, and they are often working while hungry. Is there anything comparable going on in the US?
- mrcartmeneses 4y ago
  Interesting you say that because there’s some critical analysis I’ve come across in the past that states that taxation is a key driver towards democracy and that captured wealth (such as from oil) promotes oligarchy and dictatorship. There are outliers in either direction, naturally.
jokethrowaway 4y ago
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.
- Kaapeine 4y ago
  That sounds a lot like the DARPA model too.
- mrcartmeneses 4y ago
  There’s way less inefficiency and way more accountability in the public sector. Look at how efficient publicly funded schools are, for example, or publicly funded rail or healthcare. You could literally pick almost any industry. Accountability comes from elections. If managers in companies had to be re-elected it would be interesting.
  - psi75 4y ago
    This. Corporations are great at imposing mean-spirited personal accountability (i.e., if you're perceived to have fucked up, you get fucked) but that doesn't actually solve problems or change anything. People get fired, careers end, new faces replace the old, nothing gets learned. Of course, once you get into middle management you're exempted from the stack-ranking bukkake, and executives write their own performance reviews and almost never face consequences for their actions. Companies are fantastic at making it look like accountability exists, because people at the bottom get punished for even the smallest mistakes, but avoiding any consequences that would affect high-ranking members or force the organization to change how it does business.