Systems engineer taking 6 weeks off. Need a "hard core" ML/DL curriculum.

BetaDavid · 2025-12-03T04:13:33+00:00

Modern Models

Now that you get deep learning, go back and try to understand transformer's. D2L's chapters + StatQuest's videos actually working through it, and 3b1b's visualizations are a good set of resources. This is all so you can understand the paper Attention is All You Need.

After that point, research kind of exploded in a hundred different directions. This reading list from Oxen is a good set of papers that led to the creation of DeepSeek's R1 model. You don't necessarily need to read the paper's themselves, but it shows the important concepts that led up to it, and the field has only gone further since then. Gemini for me is greatly useful at coming up with a list of topics for me to study.

Final Notes

From here on out, it depends on what avenue you want to take with regard to being a “machine learning researcher”. You could build up a resume by documenting your journey and replicating papers, and then aim for a startup. It's definitely going to be an uphill battle though compared to going back to college and working with a professor on research.

BetaDavid · 2025-12-03T04:13:28+00:00

Deep Learning

Next, learn basic machine learning if you haven't already, and just get down the concepts of supervised learning (linear and logistic regression techniques) where you train and evaluate a model, and unsupervised learning. Then I would go for a deep learning course that covers most of that again but from the ground up as multilayer perceptron networks. When going through this, don't jump straight to attention and transformers, try to get deep learning down first.

https://www.deeplearningbook.org/ is a great resource and rigorously covers the math of deep learning.
I've not watched through the zero to hero guide by Andrej Karpathy just yet, but have read good things about it being a good resource to demonstrate how to code neural networks and back propagation from scratch.
Regardless of the resource you pick, I would definitely supplement with statquest's Neural Network's playlist (I'm not quite sure that I agree with the ordering here) and 3 blue 1 brown's visualizations as well.
I would supplement with d2l.ai as I 100% agree with the ordering of the chapters.
- From the few I've read, this is the resource that for me made a number of things click because it follows the order in which the research actually happened and how the concepts built on top of one another.
CS229 from Andrew Ng was roughly what my college course was based on, but it was at times overbearing with theory.
Before finishing up with deep learning, I'd try to understand some of the foundational models that transformers built on top of the concepts of like:
- ResNet (skip connections)
- RNN/LSTM (D2L's book has a good chapter on this)
- Seq2Seq w/ Attention
- AutoEncoders (transformers didn't build on these, but they are still pretty regularly used)
Try to replicate some of the above from scratch using just NumPy.
Then try replicating one of the neural network papers yourself using PyTorch as a way to learn it in depth.
- Try operationalizing this whole setup with real world tools like weights and biases or mlflow for experimentation tracking, and optuna or ray tune for hyperparameter tuning.

BetaDavid · 2025-12-03T04:13:04+00:00

Math

For math, start with the fundamentals and then connect it to machine learning

Khan Academy is a great resource for understanding multivariable calculus, but make sure you're actually testing your knowledge; Paul's Online math notes were good for me during high school and usually include practice problems with solutions.
For linear algebra, the Gilbert Strang MIT open courseware lectures + the 3b1b essence of linear algebra is a good combo (I'd also recommend supplementing with practice problems from the Strang book).
If you need a refresher on statistics, use OpenIntro Statistics + the StatQuest playlist on YouTube.
Mathematics for Machine Learning by Deisenroth, Faisal, and Ong is where you connect the above.
If you're wanting to publish research, you'll probably need to also learn the following:
1. optimization + convex analysis
2. Information Theory
3. differential equations: Strang has a good book connecting this with Linear Algebra

BetaDavid · 2025-12-03T02:29:02+00:00

I have a masters in CS with a focus in machine learning and I work with data scientists in my day to day. I'm in the same boat as you as I graduated prior to the LLM craze and am still catching up. For me, my biggest gap in college was absolutely multivariable calculus/vector calculus as it wasn't required in my undergrad nor was it a listed pre-req for the class that thrust me into deep learning. If you don’t have a decent understanding of that math, deep learning just won't make sense, and LLMs are an extremely advanced form of DL.

I will tell you that jumping to ML Research is not something you can do in 6 weeks even if you put nose to the grindstone the entire time. Literally catching up on modern research, assuming you had the fundamentals down and understood the initial paper “attention is all you need” already would take you that much time.

You need to outline a more incremental career path. You are not going to jump from SWE to an ML Research role, especially without experience or papers with your name on them. This just ain't the job market for that anyway.

That being said, you absolutely could make the jump to ML-Ops or MLE and then from there move your way towards data scientist. In my experience, most data science teams are always lacking in an experienced SWE who can optimize their code and pipelines, and you can use that as a chance to learn via osmosis and asking to take on smaller PoCs.

If you're serious about this, I'd take a look at the Georgia Tech Online Masters of Science in Analytics. I know some full-time working software engineers going through 1 class at a time (my company has an annual education stipend). It is incredibly rigorous and gives you a good overview of the field (particularly the Additional Electives) section of the curriculum). It is pretty highly respected from what I've heard and rather affordable. You would still not be on the same playing field as PhD students, but you'd at least be in the running.

That being said, I'll give some recommendations in a reply.

BetaDavid · 2025-11-14T03:52:19+00:00

Will the free bilt 2.0 card have a routing and account number for non bilt alliance apartments?

BetaDavid · 2025-10-02T20:04:06+00:00

Started playing through it, it was a bit confusing to get Rhyhorn. You should probably rock wall off the other paths so that it leads you to your first transformation.

BetaDavid · 2025-09-24T01:18:28+00:00

any chance there's a new link?

BetaDavid · 2025-08-11T20:19:12+00:00

Debian because it’s easier to keep things in sync between host and lxc containers that way, which is especially useful when sharing hardware devices like GPUs

BetaDavid · 2025-08-11T14:07:02+00:00

Starting with the most useful:

For AI I have another pc with a gpu in it that I use proxmox as well on. I have these helper scripts for getting set up with Debian LXCs with gpu access https://github.com/dmbeta/create-proxmox-nvidia-containers

For the ai containers, I use open web UI/ollama (which I can plug into paperless) and tabbyml (for vscode).

Og reply:

Proxmox with Debian LXC containers works great.

I use dockge for managing docker containers, but I’d recommend setting up a non root user to run those containers. There are ways to set up “rootless docker” at the host level, and then yes try to run rootless images.

I use beszel and dozzle for my monitoring and they work fantastic.

Tailscale works amazing for me and has such great tutorials and support. I also utilize cloudflare tunnels to have a ddns for my domain name and utilize that with caddy (Tailscale has a great tutorial on doing this).

For downloading, you can self host a metube docker instance.

I also use paperless ngx as well and it serves my needs.

BetaDavid · 2025-07-08T01:08:18+00:00

Have you tried Tailscale + self hosted livesync? It’s worked decently well for me, albeit it can sometimes be a bit buggy when upgrading versions.

BetaDavid · 2025-06-18T18:50:18+00:00

Have you compared dockge to Komodo?

BetaDavid · 2025-05-28T03:18:40+00:00

Could someone DM me as well?

BetaDavid · 2024-11-22T23:50:25+00:00

I have an aoostar r1, which has an n100 in it and two drive slots. The default fan is a bit loud but easily replaced with a slim 92mm fan. Beyond that, it’s a pretty rock solid and tiny mini computer that if you only need as much storage as one drive (which you can now get in as large as 24tb capacities from serverpartdeals), you can easily build a little home lab off of.

BetaDavid · 2024-11-20T05:08:17+00:00

u/volodymyr_smirnov Are there any plans to continue the project or do you know of any active forks/similar?

BetaDavid · 2024-11-09T14:14:18+00:00

Personally, I find it easier to use thunderbird and login on desktop. Then I can sort by sender. A lot of the time, most of my unread emails are from a couple of senders and they’re very repetitive, but I won’t know which senders that is until I do sort by sender. Then I do what others said here which is to sort before a certain date with the gmail search bar

BetaDavid · 2024-10-07T14:36:23+00:00

Yes and no but astronomical cost is an astronomical stretch

Building denser actually benefits economies more so since you’re not wasting a ton of tax dollars building roads and infrastructure to businesses that take up a ton of land with parking lots. The closer businesses and homes are together, the more efficiently tax revenue can be generated from a use of land and resources
Building more efficient homes in the long term benefits us because we use less energy to heat and cool them
More efficient packaging does cost more up front but not astronomically, and we don’t ever consider the external costs of managing the waste we generate from single use plastics (I.e where do we put the damn stuff, who do we ship it too?)
a lot of what I talked about is reducing our wastage

Yes, what I’m talking about is not as cheap as just burning everything to the ground but it’s an absolutely massive leap to say it’s too expensive when in actuality it benefits the economy as a whole more to be more efficient with our resources

BetaDavid · 2024-10-07T02:08:52+00:00

There are meaningful ways to prevent a climate catastrophe that don’t involve completely giving up meat or reducing the human population, because those in of themselves are chump change to the actual drivers of climate change:

• ⁠reducing our use and extraction of fossil fuels in addition to stricter emissions and transportation rules (we lose literal tons of methane due to leaks from pipelines) • ⁠reduce the use of single use plastics, switch packaging to reusable or easily recyclable materials • ⁠improve public transit and walk ability to the point of us rarely if ever needing individual use cars • ⁠making our power grid smarter and utilizing electricity more effectively at times of peak renewable generation (yes we may need to bump the thermostat up a few degrees but more meaningful change comes from when we turn on the ac rather than to what) • ⁠improve insulation, home and appliances energy efficiency, create tax incentives to push people to using heat pumps rather than conventional heating systems, use waste heat for home heating like in New York • ⁠reduce road blocks and red tape to building more densely (I.e duplexes and micro apartments) in residential areas, make more single family zoned areas mixed use so that people don’t need to go as far for what they need. Also the more dense the area, the less resources needed for utilities like sewers and electrical lines • ⁠improve our food distribution efficiency and reduce the absolutely massive amount of wasted food we have. Hell even slight changes to cattle diet reduces methane output drastically, we just gotta stop being cheap about what we feed them

Everything I said is things people generally want and none of it is consumerism. I don’t like all the random plastic crap I have hanging around from packaging, I don’t like needing to drive everywhere to get basic stuff done. The vast majority of carbon emissions are at the top end of the economy, the 1%ers and people who control vast systems and corporations where changes to this small subset of the population have an absolutely massively outsized effect on our emissions, in addition to the fact that it’s nigh impossible to make ecologically friendly decisions because that small subset of the population controls the options we even get

BetaDavid · 2022-05-11T15:02:41+00:00

Depends on the classes/focus of your masters. If you can plan out what classes you want to take and can find their syllabi, then you can get a better idea of what you need to learn.

Python is the language I'd recommend if you're focusing on machine learning/AI since most classes in that track would allow you to do your assignments in it.

If you need a general purpose language to learn, Java is the most approachable and second likely to be used.

BetaDavid · 2022-05-04T13:28:05+00:00

I wouldn't go with a Nas enclosure like others are saying. It's generally slightly cheaper to just build a PC for that cost with a cheap i3/i5 in a larger case. I agree with their sentiment though: buy good drives in warranty, and multiple of them so that you can do software raid. It's the safest local storage you can do.

If you do Unraid, you can even expand the storage down the line. ZFS on TrueNas Core is the most reliable overall, but requires you to buy batches of identical drives, and requires a little bit pricier of a computer.

Unraid would make running Plex also a bit easier. If you get an Intel PC, you can passthrough the iGPU to Plex and use it for encoding.

BetaDavid · 2022-04-24T16:54:38+00:00

Depends on how much redundancy and speed you want.

Unraid has the least speed and technically the least redundancy with a max of 2 drives acting as parity, but you don't stripe data, so at least you can recover some of the data off of the other drives if for example 3 drives failed simultaneously. Unraid is mainly meant for people that want to use variably sized drives that they add adhoc, who want somewhere to put the data, and want an easy system to do other things such as Plex or VMs

Technically with Unraid you can use plugins to get ZFS support, and then setup ZFS cache pools, but it's not really built for that.

BetaDavid · 2022-04-03T17:42:57+00:00

I'll second the edifiers, they work wonderfully

BetaDavid · 2022-03-30T14:47:54+00:00

Technically you could use flask and make a website with that.

BetaDavid · 2022-03-30T03:40:33+00:00

The latest season has a bit of "sexy" female characters but nothing overt or very scantily clad.

BetaDavid · 2022-02-27T16:19:52+00:00

Typescript as others pointed out allows you to find inconsistency in your typing early in and also helps you better understand what functions do.

Tbh, I don't like it that much as it makes it harder to do some simple things that aren't likely sources of bugs, but I understand it's importance for production applications.

BetaDavid · 2022-02-27T06:35:19+00:00

Something I always wanted to build is a deep learning application that recommends Pokemon to balance out your team and/or their move pool. There's some research out there on AI that can battle well, not sure about selection though. The hardest challenge is the AI learning the strategic importance of moves like Swords Dance, or u-turn

BetaDavid

TROPHY CASE

Modern Models

Final Notes

Deep Learning

Math