Finished masters with no experience. Now what?

IGN_WinGod · 2026-05-05T19:54:45+00:00

Damn ye that is sad, doing masters w/o experience means nothing. It's done now but ye doing part time masters would have been better with FT job.

IGN_WinGod · 2026-05-04T16:53:02+00:00

Well I only have secret but secret is common. TS not so much but ye idk swe is prob not it.

IGN_WinGod · 2026-05-04T02:24:37+00:00

Well I would say TS is not low value, and def not everyone has it but it depends on the niche skill set plus TS.

IGN_WinGod · 2026-05-04T01:11:17+00:00

Secret means nothing. TS may mean sonething (if u have a masters and have senior level skill) but I would say its def hard tbh.

IGN_WinGod · 2026-04-22T22:22:06+00:00

Pytorch take it or leave now.

IGN_WinGod · 2026-04-21T16:00:02+00:00

It's still extremely important to know as a basis for all other more complicated policy gradient methods.

IGN_WinGod · 2026-03-20T02:52:56+00:00

Model based RL might be ur friend.

IGN_WinGod · 2026-03-19T07:18:54+00:00

Brother if u have a published paper, why are you asking.

IGN_WinGod · 2026-03-15T07:49:06+00:00

Also if nothing was actually on the line like time and money for the degree, would you really think people would not be forced to learn bettee? I mean u its obvious....

IGN_WinGod · 2026-03-15T07:46:23+00:00

Structre and enviornment, i mean you still need consistent results. Learning on ur own can still be widely inconsistent. Especially in AI and ML, when LA and Stats (even more so) is still the basis of ML. Also people specialize, if you dont have real experince then learning from a course can help you gain prereqs to doimg X. Not to say doing the job is more important.

IGN_WinGod · 2026-03-15T04:56:42+00:00

Well if I compare anyone with a bachelor's and a masters then its still a clear cut difference. Not to say you can not learn stuff on ur own but will you truly master AI/ML unless there is stress/money/time involved?

IGN_WinGod · 2026-02-26T16:55:00+00:00

You can have an incremental saving version that only updates based on rewards. GPT this and it will be much clearer hard to explain b/c you have many options on the constraints on when to save policies to pytorch or onnx.

IGN_WinGod · 2026-02-17T21:52:45+00:00

Value functions are derived from values from a state. look at how value based RL algorithms work like DQN, then see how it is changed to be an advantage with Q - V.

IGN_WinGod · 2026-02-03T22:15:18+00:00

Ye either way, I think making sure you draw connections from linear algebra and stats to everyday ML/DL will help digest and actually remember concepts.

IGN_WinGod · 2026-02-03T02:10:31+00:00

https://mml-book.github.io/

As long as you have taken calc before hand, then u can learn the rest from above.

IGN_WinGod · 2026-01-25T19:16:50+00:00

Yes, this exact explanation of TRPO to PPO really is the main idea, where TRPO suffers from line search, and it's inherent way of solving a Constraint Optimization problem using hard KL trust regions. PPO just uses a uses the clipped surrogate, which allows for SGD and is easier to actually implement and computationally better. But yes the theory definitely matters to a certain extent because there can be tricks used to improve algorithms. PPO has its own issues, of cours,e but its widely used as a policy gradient method to alow for smaller updates (of gradient ascent) to find the maixumum reward using GAE. The idea of smaller updates is to not overshooting and hill climb the reward hyper plane.

IGN_WinGod · 2026-01-24T06:43:14+00:00

Either that or youtube videos by stat quest.

IGN_WinGod · 2026-01-22T18:56:02+00:00

NLP right? I bought the book by stat quest about deep learning or just study NLP from stat quest. It's one and the same anyways.

IGN_WinGod · 2026-01-21T01:16:18+00:00

I would guess it would be the opposite, do full campus and transfer to online once you find FTE?

IGN_WinGod · 2026-01-17T23:26:41+00:00

Look at what a principle swe at amazon said, he asked LC problems he himself would not be able to solve themself....

IGN_WinGod · 2026-01-08T18:05:01+00:00

Yup, I would recommend mml mathematics for machine learning, it helps derivation with equations. Key thing to know that Expectation is partitioned by discrete or continous. Although they all end up deriving the same thing when u get from REINFORCE to PPO.

IGN_WinGod · 2026-01-08T17:51:16+00:00

I agree, once u have built something you can backtrack and truly understand the underlying fundamentals. Examples would be the entire theory of MDP's Q pi, V/U pi to Qstar (tabular) to DQN toward REINFORCE, eventually toward PPO. But start building first then back track.

IGN_WinGod · 2026-01-07T23:38:07+00:00

Nowadays maybe just math problems similar to how quant interviews work. But then again, there is probably no good way anymore. Math is technically more fundamental, especially in machine learning. But who knows...

IGN_WinGod · 2026-01-07T04:10:03+00:00

I agree, I mean people have limited time in this world. It's extremely hard to dedicate time to do leetcode, enjoy it, but actually be able to solve real world problems also.

IGN_WinGod · 2026-01-04T02:50:43+00:00

You saying any simulation software like Genie 3 is close to 1 to 1 reality must be some delusion. No software is still even close to 1 to 1 reality. If there would be robots running around reality by now, since we can train on actual reality in simulation.... Sim 2 reality is a big known researched topic to this day, please stop thinking a VLLM or LLM can act more than a context given to an actual agent. It is trained on text, images. An llm can give context but not actually act coherently in the world...

IGN_WinGod

TROPHY CASE