The single thing which I learned the most from was implementing a paper. Lectures and textbooks to me are just words. I understand them in the abstract but learning by doing gets you far deeper knowledge.

Others might suggest a more varied curriculum but to me nothing beats a one hour chunk of uninterrupted problem solving.

Here are a few suggested projects.

Train a baby neural network to learn a simple function like ax^2 + bx + c.

MNIST digits classifier. Basically the “hello world” of ML at this point.

Fine tune GPT2 on a specialized corpus like Shakespeare.

Train a Siamese neural network with triplet loss to measure visual similarity to find out which celeb you’re most similar to.

My $0.02: don’t waste your time writing your own neural net and backprop. It’s a biased opinion but this would be like implementing your own HashMap function. No company will ask you to do this. Instead, learn how to use profiling and debugging tools like tensorboard and the tf profiler.

I would start by taking a free university level course in statistics. Then I would continue with the basics: SVM, linear regression, naive Bayes, gradient boosting, neural nets etc. I would not only train and fine tune them, but I would also build simple ones myself instead of just using libraries. Then I would continue to what you said, participate in Kaggle competitions, try to solve real world problems.

I think that understanding the field from bottom up is priceless. Many people fine tune and train models, but they don't understand how that model works, nor do they know if the model they've chosen is the best fit for the problem they are trying to solve.

It's a rather long path if you really want to get good at it. Like in music: you can learn to play a tune by ear or you can learn to have a good, deep and thorough understanding of music.

Besides, you are also wrong, having good fundamentals in the maths will help you pick up new methods much faster as they pop up. And especially if you want to come up with new methods (as in research), there are no shortcuts.

Abstraction helps you to be productive. It's certainly good to understand everything all the way down to the actual physics behind computing, but its not necessary especially at the start. I don't have to know exactly how logic gates work to program in JS and make a difference. I assume the same applies to ML. Motivation is what prevents most people from learning difficult to learn skills. Finding ways to produce value early on can help keep you motivated.

I don't regard time spent in University to learn how logical gates work and many other useful things, as a loss of time. And, as a web developer/architect (after many other industries I worked in), I typically make more money than peers who don't. And knowing how things work, helped me immensely in my career. Not everything is solvable by looking on Stack Overflow.

It depends on what professional level you are content with.

Learning was not only motivated by money - for me- I was genuinely curious about computers and software since I was a kid.

It’s unquestionably a loss of time, the only question is whether it’s an optimal use of time. This depends on your goals. For most software engineers looking to use ML, starting with existing frameworks and knowledge and drilling down as necessary is the most prudent method.

We all have to make sacrifices in what we learn. Even for yourself, the topics you chose to learn about implicitly left out other parallel topics you didn’t learn about. And you also didn’t learn everything from the least abstracted, most fundamental level. We need to choose the appropriate level of abstraction for the problem at hand, which will depend on each person’s goals.

Thats a stupid analogy to the above discussion. A better analogy would be trying to program in JS without understanding for loops and basic programmation concepts.

Thats typically what I observe with younger folks jumping into neural nets directly. They have a very shallow understanding of anything and survive with youtube tutorials.

I'm all for learning the fundamentals properly - but those fundamentals are going to be completely things, things like core principles of statistics (limitations of correlation, confounders, bias/variance, etc), the relevant parts of calculus and linear algebra that matter for understanding optimization, the best practices for management of data, experiments and measurement to not cheat yourself, etc - not the checklist of many different, parallel methods of machine learning like decision trees or reinforcement learning, which are both useful and interesting, but not related or required to properly apply e.g. transformer-based large language models for your task.

> without understanding for loops and basic programming concepts

Depending on what you mean by “understand” I would guess most software engineers don’t “understand” for loops either. For loops are an abstraction built on the CPU instruction set provided by each programming language. We use them with the knowledge of how they behave, which is the correct level of abstraction nearly 100% of the time.

And in CS undergrads, we don’t throw people into a course on assembly first. First they learn something like Java or Python, and only later dig deeper into more fundamental concepts. That’s not an accident.

I for one would be interested in learning more foundational stuff; I have no interest (though perhaps that process would change this!) in a particularly ML job, and certainly not in learning how to point and click and run other people's work from a YouTube video with a scream-face thumbnail.

For those who feel similarly, I asked about it recently, maybe something you like the look of:

https://news.ycombinator.com/item?id=38320244

I also came across (doesn't seem it was mentioned there) Understanding Deep Learning (Simon Prince) which looks like it might be good:

https://mitpress.mit.edu/9780262048644/understanding-deep-le...

Of course not. When you learn this way you will realize there are a myriad of problems that can be solved with more simple algorithms. Trying to make every problem fit to neural networks is pure cargo culting.

For me, playing with things and doing cool & fun stuff is always the way to get deeper into something.

Stats is one of a very small number of college courses that I took where I came away thinking "this should be mandatory for all voting adults". I use that stats course way more often than I use even algebra, just to be a functional adult in a world where bad statistics are used day in and day out to manipulate and deceive people into buying things or voting for someone.

So, I have to disagree: not only is a basic foundation in stats essential for understanding ML, it's something everyone really should have under their belt anyway to live in the modern world without turning into someone else's pawn.

When I studied ML in 2012, the very first course started with naive Bayes and went one from there. A decade after being away, I see a lot of people around me starting with neural nets to train a model that naive Bayes would be plenty enough for and never heard about naive Bayes. Is that only my experience?

But also there are many tasks for which naive Bayes works, but a NN solution can be much more accurate if you're okay with it being also much more compute-intensive. E.g. things like sentiment analysis or simple spam filters are often used as a demonstration of naive Bayes, but you can do much better with more powerful models.

I don't think you should be combining writing a neural network with doing backprop since I don't know anyone working with serious ML who is not using some sort of automatic differentiation library to handling the backprop part for them. I'm not entirely sure people even know what they're saying when they talk about backprop these days, and I suspect they're confusing it with gradient optimization.

But anyone seriously interested in ML absolutely should be building their own models from scratch and training them with gradient descent, ideally start with building out your own optimization routine rather than using a prepackaged one.

This is hugely important since the optimization part of the learning is really the heart of modern machine learning. If you really want to understand ML you should have a strong intuition about various methods of optimizing a given model. Additionally there are lots of details and tricks behind these models that are ignored if you're only calling an api around these models.

There's a world of difference between implementing an LSTM and calling one. You learn significantly more about what's actually happening by doing the former.

It’s an important component but I wouldn’t say it’s the main factor. ML is ultimately about your data, so understanding it is critical. Feature selection and engineering, sampling, subspace optimization (e.g. ESMMs) and interpreting the results correctly are really the main places you can squeeze the most juice out. Optimizing the function is the very last step.

Basically, you can go ahead and optimize down to the very bottom of the global min but a model with better features and better feature interactions is going to win.

Further, there are a ton of different optimizers available. SGD, Adam, Adagrad, RMSProp, FTRL, etc. With just one hour a day, you could spend six months simply writing and understanding the most popular ones.

I have a masters degree in computer science and took a fair share of ML graduate courses. That pretty much summed up what I was thinking. They basically forced me to sit and learn something I wouldn't have alone.

Now-- I'm not saying you need to go to grad school. You could buy some ML textbooks and force yourself through them and go from there... but how many people have that grit? I wouldn't have been one of them :)

It doesn't have to be production ready of course, but spending 3-4 hours to write it out in code, debug a few steps, ... are useful in my opinion.

or at least watch the karpathy video and try to follow along

I think it's worth doing a very simple implementation at least once to ensure you have the fundamentals memorized. It's not actually that complicated to implement a very simple one. Maybe a day or two -long project

Don't worry about what companies will ask you to do unless you absolutely have to.

Do you have any starters on how one selects papers in the early days, to implement?

Also - any great papers you recommend beginners expose themselves to?

I wouldn’t recommend papers to absolute beginners though. For them, it’s best to go to HuggingFace, find a model that seems interesting and play with it in a Jupyter notebook. You’ll get a lot more bang for your buck.

Those papers were within reach for me. Even if the math (or the collossal search effort) needed to find them was out of reach, implementing them wasn't.

There were some things besides optimizers and activation functions too. In particular I remember Dmitri Ulyanov's "Deep Image Priors" paper. He did publish code, but the thing he explored - using the implicit structure in a model architecture without training (or, training on just your input data!) is actually dead simple to try yourself.

I'm sure if you just drink from the firehose of the arxiv AI/ML feeds, you'll find something that tickles your interest that you can actually implement. Or at least play with published code.

Most of the action happens in python. That being said, there’s a library called Tensorflow JS. It has some pre-trained models you can use off the shelf and run from your browser. Things like face detection and sentiment analysis.

You say don't write your own neural net and backprop implementation. That makes sense to me. What do you suggest using instead, for your suggested projects? I'm guessing tensorflow, based on your suggestions on profiling and debugging tools? Do the papers / projects you suggest map straightforwardly onto a tensorflow implementation, rather than a custom one?

That’s true. Or if you’re really rich I guess.

I assume you’re talking about the latest advances and not just regression and PAC learning fundamentals. I don’t recommend following a linear path - there’s too many rabbit holes. Do 2 things - a course and a small course project. Keep it time bound and aim to finish no matter what. Do not dabble outside of this for a few weeks :)

Then find an interesting area of research, find their github and run that code. Find a way to improve it and/or use it in an app

Some ideas.

- do the fast.ai course (https://www.fast.ai/)

- read karpathy’s blog posts about how transformers/llms work (https://lilianweng.github.io/posts/2023-01-27-the-transforme... for an update)

- stanford cs231n on vision basics(https://cs231n.github.io/)

- cs234 language models (https://stanford-cs324.github.io/winter2022/)

Now, find a project you’d like to do.

eg: https://dangeng.github.io/visual_anagrams/

or any of the ones that are posted to hn every day.

(posted on phone in transit, excuse typos/formatting)

https://karpathy.ai/zero-to-hero.html

Doing fastai while solving comparative problems on your own in kaggle is quite enlightening

Leveraging existing LLM technologies and putting them in software where regular people can use them and have a great experience is important, necessary work. When I studied CS in college the data structure kids were the “cool kids”, but I don’t think that’s the case in ML.

The daily practice is to sketch applications, configure prompts and function calls, learn to market what you create, and try to create zero to one type tools. Here’s two examples I made, one where I took the commonplace book technique of the era of Aristotle and put it in our modern embeddings era [1] and one where I really pushed to understand the pure MD spec and integrate streaming generative models into it [2]

[1] - https://github.com/bramses/commonplace-bot

[2] - https://github.com/bramses/chatgpt-md

If you don't have a solid enough footing to get a job in the field yet, the next best thing in my opinion: find a passion project and keep cooking up new ways to tackle it. On the way to solving your problem, you'll undoubtedly begin absorbing the tools of the trade.

Lastly, consider going back to school (a Bachelor's or Master's, perhaps?). It'll take far more than 1 hour/day, but I promise you, you'll see results far faster and far more concretely than any other learning strategy.

Good luck!

Context: I've been a Researcher/Engineer at Google DeepMind (formerly Google Brain) for the last ~7 years. I studied AI/ML in my BS and MS, but burnt out of a PhD before publishing my first paper. Now I do AI/ML research as a day job.

The problem with projects is one's understanding tends to go more and more specialised, and collaborating/connecting with other ML engineers requires a broader knowledge base sometimes.

Also, for giving advice and useful inputs to others (on their projects), I feel a balanced knowledge base is useful.

Hence the question.

I think it'll help if you can get a job at a company who's main focus is ML, you'll talk to folks who are doing research or solving problems using ML, you'll learn. If not, i hope these links help as folks there (people way smarter than me, a swe) had similar question and documented the steps they took to reduce the gaps in their understanding.

[1] - https://blog.gregbrockman.com/how-i-became-a-machine-learnin... [2] - https://agentydragon.com/posts/2023-01-11-how-i-got-to-opena... [3] - https://github.com/jacobhilton/deep_learning_curriculum

0) Learn the pre-requisites of math, CS, etc. That usually means calc 1-3, linear algebra, probability and statistics, fundamental cs topics like programming, OOP, data structures and algorithms, etc.

1) Elementary machine learning course, which covers all the classic methods.

2) Deep Learning, which covers the fundamental parts of DL. Note, though, this one changes fast.

From there, you kind of split between ML engineering, or ML research.

For ML engineering, you study more technical things that relate to the whole ML-pipeline. Big data, distributed computing, way more software engineering topics.

For ML research, you focus more on the science itself - which usually involves reading papers, learning topics which are relevant to your research. This usually means having enough technical skills to translate research papers into code, but not necessarily at a level that makes the code good enough to ship.

I'll echo what others have said, though, use to tools at hand to implement stuff. It is fun and helpful to implement things from scratch, for the learning, but it is easy to get extremely bogged down trying to implement every model out there.

When I tried to learn "practical" ML, I took some model, and tried to implement it in such a way that I could input data via some API, and get back the results. That came with some challenges:

- Data processing (typical ETL problem)

- Developing and hosting software (core software engineering problems)

- API development

And then you have the model itself, lots of work goes toward that alone.

This has helped me build the intuition for understanding these concepts in ML, and as an experienced developer I've found I've been able to pick up the ML stuff relatively easily - it's mostly libraries at the practical level. This has in turn shown me two things: ML is data quality, prep, and monitoring; I actually like the maths: it annoys me that there's this whole branch of knowledge that I don't grok intuitively and I want to know more. As I go deeper on the maths, I find myself retrospectively contextualising my ML knowledge.

So: do both and they'll reinforce each other - just accept you'll be lost for a bit.

Also: working with LLMs is incredible, as you can skip the training step and go straight to using the models. They're fucking wild technology.

From my time in college studying this, you had approximately four types of students:

1) Those that didn't understand how models worked, and lacked the math to theoretically understand the models (dropped out class after a couple of weeks)

2) Those that understood (intuitively) how the models worked, but lacked the math to read and formalize models. Lots of students from the CS program fell under this group - but I think that is due to CS programs here having less math requirements than traditional engineering and science majors.

3) Those that understood how the models worked, and had the math knowledge. This was the majority of students.

4) Those that did not understand the models, but had the math knowledge.

Of these, 2-3 were the most common types of students. In the rare occasion, you had type 4 students. They would have no problem with deriving formulas, or proving stuff - but they'd more or less freeze up or start to stumble when asked to explain how the models worked, on a blackboard.

With that said, if someone has any ambition of doing ML research, I think math prereqs are a must. Hell, even people with good (graduate level) math skills can have a hard time reading papers, as there are so many different fields/branches of math involved. Lots and lots of inconsistent math notation, overloading, and all that.

There's a lot of contrived "mathiness" in papers, even where simple diagrams will do the trick. If your paper doesn't include a certain amount of equations / math, people aren't taking it serious...so some authors will just spam their papers with somewhat related equations, using whatever notation they're most comfortable with.

For example, gradient descent in conjunction with your learning rate can be visualized as calculating your error vector (your gradient), stretching it by your learning rate, applying it your parameters, computing the next error vector, and so on. If you think of what applying this vector might look like in 3d space, training your model is basically getting all your parameters to fall into a hole (an optimum). This kind of conceptualization helps you understand the purpose and impact of the learning rate: a way to stretch out the steps you make to descend into holes, so that you might hopefully "shoot over" local non-global optima while still being able to "fall into" other optima.

You could read papers and stare at code for a very very long time without developing that kind of intuition. I don't think I could ever come up with this myself just dabbling.

And as a side note, in mathematics at least for me, the most unexpectedly hugely important factor in understanding something is exposure-time. In college and grad school I found I didn't fully intuit most material until about 12mo after I had finished studying it - even if I hadn't actively studied it at all in the interim. I think it has something to do with the different ways our brains encode recent/medium/long term knowledge, or sleep, or something - not really sure, but I do know the earlier you started learning something and exposing yourself to the concepts, the sooner your subconscious builds that intuitive understanding. So you can do yourself a huge favor by just making an effort to dive into the math material even if it feels like a slog or that you're not getting it right this minute - you might make up one day in a few months and just "get it" somehow

I worked for a major company on an ML project for 2 years. By the time I left, I realized that:

1: The project I was working on has no improvement over ordinary statistical methods; yet the ability for people to understand the statistics (over the black box of ML) meant that the project had no tangible improvement over the processes we were trying to replace.

2: A lot of the ML I was working on was a solution in search of a problem.

I personally found the ML system I was working on fascinating; but the overconfidence about what it can infer, and the way that non-developers thought ML could make magical inferences, frustrating.

---

One other thing: Make sure you understand how to use databases, both SQL and non-SQL. In order to use ML effectively, you will need to be excellent at programming with large volumes of data in a performant manner.

answering that requires a good understanding of the problem at hand as well as knowledge to be able to propose a simple solution that could be the starting point, and then searching for improvements over the same - assuming the improvement they bring is useful to the solution at hand.

i guess what I am trying to say is, the question asked by op and your suggestion are orthogonal imo :)

Things like ML ops, application of DevOps, testing and ci/cd in the ml space, how to train across multiple gpus, how to actually host an LLM especially at scale and affordably.

In my experience there are hundreds of candidates coming from academia with strong academic backgrounds in ML. There are very few experienced engineers available to help them realise their ambitions!

https://research.google/pubs/whats-your-ml-test-score-a-rubr...

Note they start to discuss things like unit testing, integration testing, processing pipelines, canary tests, rollbacks, etc. Sound familiar yet?

The same author has also written this book:

https://www.oreilly.com/library/view/reliable-machine-learni...

I don't see a software engineer's skills becoming redundant in this field, especially if you have a good level of experience in cloud infra and tooling. It seems more valuable that ever to me (e.g. I have worked with ML Researchers who don't grasp HTTP let alone could set up a fleet of severs to run their model developed entirely in Jupyter Notebook).

I have found it helpful to equate myself with the correct tools and terminology in order to speak the right language - there's specific tools lots of people use such as Weights & Biases for "Experiment Tracking", terms like "Model Repository" which is just what it sounds like. "Vector Databases" (Elastic Search had this feature for years), "Feature Stores" - feel familiar to big table type databases.

Reading up on a typical use case like "RAG - Retrieval Augmented Generation" is a good idea - alongside starting to think about how you'd actually build and deploy one.

Above all having a decent background in cloud infra, engineering and how to optimise systems and code for production deployment at scale is a very in demand at the moment.

Being the person helping these teams of PHDs (many of whom have little industry experience) to productionise and deploy is where I am at right now - it feels like a fruitful place to be :)

Can you recommend any learning resources for things you mentioned? I don't have an option to learn these on my current job, so it will be hard to structure CV to prove my future employers I know them when I don't have real world experience.

2. Keep a couple of Mathematics/Statistics books handy while you are going through the above. When the above book talks about some Maths technique you don't know/understand you should immediately consult these books (and/or watch some short Youtube videos) to grasp the concept and usage. This way you learn/understand the necessary Mathematics inline without being overwhelmed.

This is the simplest and most direct route to studying and understanding AI/ML. Everything else mentioned in this thread should only come after this.

Personally I think the best way to develop per unit time is probably to try to re-implement some of the big papers in the field. There's a clear goal, there are clear signs of success, there are many implementations out there for you to check your work against and compare and learn from.

Good luck!

Try out the "historical papers"! :)

https://docs.google.com/forms/d/e/1FAIpQLScbWN3qwqeIc0b1cCRq...

Note this is for absolute LLM beginners, not if you’re already working with LLMs -- but even some of these folks have found it useful!

Hope you find this useful.

We work together to build AI models in our favorite programming languages.

- Following along with Karpathy's videos, which has been mentioned: https://karpathy.ai/zero-to-hero.html

- About to follow along with CS 231n, also mentioned: https://www.youtube.com/watch?v=NfnWJUyUJYU&list=PLkt2uSq6rB...

- Trying ideas and theories in a Jupyter notebook

- Reading papers

I would agree with other commenters that recommend learning how to implement a paper. As someone who barely managed to get their undergraduate degree, papers are intimidating. I don't know half the terms and the equations, while short, look complex. Often it will take me several reads to understand the gist, and I've yet to successfully implement a paper by myself without checking other sources. But I also know that this is where the tech is ultimately coming from and that any hope of staying current outside of academia is dependent on how well I can follow papers.

I've been doing this for about a month now, and I feel I definitely understand more of the theory of how most of this stuff works and can train a simple attention based model on a small-ish amount of data. I don't feel I could charge someone money for my skills yet, but I do feel that I will feel ready with about 6 months - 1 year of doing this.

I can do my job but I always wanted to learn and understand more. Family circumstances mean I can't afford to quit my job or go to school.

Implementing code from a paper is almost its own skillset. Papers are often math heavy and they are information dense, with lots of references to exiting works. They are designed for communicating to other researchers in the same field/niche.

Learning how to effectively prompt an LLM is an enormous space in its own right - and there's no shortcut for it, you have to actively play with the things.

I've been using them constantly for over a year at this point and I'm still figuring out new tricks and strategies all the time.

Weirdly, knowledge of Machine Learning isn't actually that relevant to getting good at using LLMs to solve problems and build software.

Knowing how to train your own neural network will do little for your ability to build astonishingly cool software on top of existing LLMs.

Knowledge of how LLMs work is useful, because it can help you prompt them more effectively if you understand their limitations, have an idea of their training data etc.

I've seen people (who I respect) argue that deep knowledge of ML can be a disadvantage when exploring LLMs, because it can limit the way you think about and interact with them. Weird but possibly true!

- 15 Rules For Crafting Effective GPT Chat Prompts (https://expandi.io/blog/chat-gpt-rules/)

- Awesome ChatGPT Prompts (https://github.com/f/awesome-chatgpt-prompts)

For more resources of like nature, you can search for "mega prompt".

One is to reproduce recent papers for which the data is available and especially if the source code is available. Don’t look at their source code initially but use it if you get stuck as a debugging method (my model isn’t converging, do they get the same gradients given the same data?)

Another is a fun idea to play with: sports data sets. Of course you have to like at least one sport but there’s lots of sports data out there that is easy to download in convenient formats (especially for baseball, where professional statisticians have been employed to do analysis since at least the 50s, but afaik all the major sports have good records these days) and you can go a long way with simple models. I’ve wasted a lot of time on the weekend coming up with fun baseball analyses.

For courses, Andrew Ng’s classes have always been good, starting with his Stanford ML class, Coursera deep learning classes, and now his short mini-classes on being an effective LLM practitioner.

Textbooks on LLMs are likely to quickly be out of date, at least I struggle to keep my LangChain/LlamaIndex book current.

My advice to you is to try to get into a paid AI job as your highest priority, and that is a lot of work: identifying possible employers, preparing for interviews, and having persistence. Some of the interesting AI work you might find will not be with “tech” companies, but rather small or medium profitable businesses that need to use ML, DL, LLMs lightly - just a small part of their successful businesses.

I would not recommend doing many things at once.

Finding resources for hands-on practice was tough, so my friend and I built Tensorgym (https://tensorgym.com), a site with practical ML exercises that increase in complexity.

The deeplearning.ai math basics for deep learning, seems self-contained. MiniTorch repo (implement your own tiny torch) seems also helpful to understand what goes on during training. MinGPT repo (to understand a basic version of GPT model structure) Dive into deep learning (textbook avail online, more focused on practical DL)

I am a Python developer who has never worked on ML/data science before, I am mostly into Data Engineering

Python is great for mixing API calls, document formatting, and other data scraping.

For myself, the problem was finding something interesting to do — in my case, generating videos from basic prompts.

I’m working off a 4GiB system with no dedicated GPU — and the Python APIs for OpenAI work fine, but admittedly, I have a Linux system.

Since you mention SE, I'd choose a mini project in an area you love. The tooling you will learn along the way.

An hour a day is paradoxically not nearly enough, yet also a serious time investment of your day.

Maybe start by asking what exactly you want to learn? Applying ML to a practical problem, in user app? The math? The ideas?

essentially, don't get paralysed on designing the perfect path on how to invest time/energy. just focus on putting in the hours everyday.

Original transformer paper: https://arxiv.org/abs/1706.03762

Illustrated transformer: http://jalammar.github.io/illustrated-transformer/

Transformer visualization: https://bbycroft.net/llm

minGPT (Karpathy): https://github.com/karpathy/minGPT

---

Next, some foundational textbooks for general ML and deep learning:

Elements of Statistical Learning (aka the bible): https://hastie.su.domains/ElemStatLearn/

Probabilistic ML: https://probml.github.io/pml-book/book2.html

Deep Learning Book (Goodfellow/Bengio): https://www.deeplearningbook.org/

Understanding Deep Learning: https://udlbook.github.io/udlbook/

---

Finally, assorted tutorials/resources/intro courses:

Beyond the Illustrated Transformer: https://news.ycombinator.com/item?id=35712334

AI Zero to Hero: https://karpathy.ai/zero-to-hero.html

AI Canon: https://a16z.com/2023/05/25/ai-canon/

LLM University by Cohere: https://llm.university/

Practical Guide to LLMs: https://github.com/Mooler0410/LLMsPracticalGuide

Practical Deep Learning for Coders: https://course.fast.ai/Lessons/part2.html

---

Hope that helps!

"Elements" is a great reference book, but it isn't really a textbook. There's a popular introductory textbook by some of the same authors called "An Introduction to Statistical Learning", which focuses on applications but elides a lot of the mathematical details.

1. Build small projects in the area you have passion about, examples: try to beat benchmark, classify news and track stories of your interest, build auto manga generator

2. Kaggle competitions: not sure if employers are looking at this though

3. Write blog about your journey.

up to your motivation, doing basic level courses first (as shared by others) and then tackling your own application of the concepts might be the way to go.

i also observe the need for strong IT skills for implementing end-to-end ml systems. so, you can play to your strenghts and also consider working on MLOps. (online self-paced course - https://github.com/GokuMohandas/mlops-course)

i went back to school to get structured learning. whether you find it directly useful or not, i found it more effective than just motivating myself to self-learn dry theory. down the line, if you want to go all-in, this might be a good option for you too.

1. Don't waste your time on courses [not after you know the basics]

2. Kaggle Competitions [Featured ones] worked for me

3. Read blogs/newsletters - Tldr AI comes with new research and many open-source projects, I have personally starred a ton of repos and it's totally amazing, then there's bizzaro devs, data elixir, Hackernews newsletter which combines top links, You can read Lilian Weng if you have strong fundamentals, Jay Alammar

4. Additionally I took Udacities nano degrees, they were nice, you can try it, for RL and Self Driving cars at least..

Best Jay

For the least technical, I suggest MattVidPro AI on YouTube.

For the slightly more technical, I suggest 1littlecoder also on YouTube.

https://ollama.ai/library

https://bbycroft.net/llm

Just acquire skills for the sake of it?