#machine learning engineers

[mitsde123]

Data Science Job Market : Current Trends and Future Opportunities

The data science job market is thriving, driven by the explosive growth of data and the increasing reliance on data-driven decision-making across industries. As organizations continue to recognize the value of data, the demand for data scientists has surged, creating a wealth of opportunities for professionals in this field.

[mochasucculent]

Good time to be reading Higurashi to remind myself I have to keep trying lol

[zooplekochi]

They call it "Cost optimization to navigate crises"

[nixcraft]

Imagine being this stupid to drink Kool-Aid and giving a remote LLM tool full access to your codebase, and, in many cases, not maintaining backups or using proper Git with permissions. How these guys are getting hired to write code is beyond me.

[hackeocafe]

How To Learn Math for Machine Learning FAST (Even With Zero Math Background)

I dropped out of high school and managed to became an Applied Scientist at Amazon by self-learning math (and other ML skills). In this video I'll show you exactly how I did it, sharing the resources and study techniques that worked for me, along with practical advice on what math you actually need (and don't need) to break into machine learning and data science.

[whispersoftheunheard]

Coding: My Escape, My Obsession

Programming—ahh, what a paradox! Sometimes it’s an absolute thrill, and other times, it’s the most stressful thing ever. For me, coding isn’t just a skill; it’s my escape. Whenever life gets heavy, my mind instinctively drifts to programming. New ideas, fresh logic, endless possibilities—it’s like therapy but with syntax errors.

But somewhere along the way, this escape became a full-blown obsession. My four years of engineering? A blur of code, projects, and fixing bugs—mine and everyone else's. I was always working, always solving something. And now, when I look back, I struggle to find those carefree moments of pure fun. Sure, I enjoyed college, but every memory somehow loops back to programming.

I don’t regret it. I don’t claim to be a coding genius either—I’m still learning, still growing. But one thing’s for sure: programming has shaped me in ways I never imagined. It gave me purpose, resilience, and a language beyond words.

Yet, here’s what I’ve realized—life isn’t just about writing perfect code; it’s about writing a story worth remembering. And while programming will always be a part of me, I want to step beyond the screen, embrace new experiences, and create moments that don’t just end in a semicolon.

Because in the end, the best code I’ll ever write is the one that balances passion with life itself.

[ccmoatyim]

PSA:

An algorithm is simply a list of instructions used to perform a computation. They've existed for use by mathematicians long prior to the invention of computers. Nearly everything a computer does is algorithmic in some way. It is not inherently a machine-learning concept (though machine learning systems do use algorithms), and websites do not have special algorithms designed just for you. Sentences like "Youtube is making bad recommendations, I guess I messed up my algorithm" simply make no sense. No one at Youtube HQ has written a bespoke algorithm just for you.

Furthermore, people often try to distinguish between more predictable and less predictable software systems (eg tag-based searching vs data-driven search/fuzzy-finding) by referring to the less predictable version as "algorithmic". Deterministic algorithms are still algorithms. Better terms for most of these situations include:

data-driven

fuzzy

probabilistic

machine-learning/ML

Thank you.

[nukacourier]

I think if Arcade and Otacon ever met it'd be soooo funny. Like I just know Otacon would be so enthusiastic and excited to talk to another scientist who seems so COOL and seems like such a nice person and meanwhile while he's talking to him Arcade is silent and just nodding along because he's trying to fight back thoughts of killing him violently with hammers

[mindblowingscience]

Researchers in the emerging field of spatial computing have developed a prototype augmented reality headset that uses holographic imaging to overlay full-color, 3D moving images on the lenses of what would appear to be an ordinary pair of glasses. Unlike the bulky headsets of present-day augmented reality systems, the new approach delivers a visually satisfying 3D viewing experience in a compact, comfortable, and attractive form factor suitable for all-day wear. “Our headset appears to the outside world just like an everyday pair of glasses, but what the wearer sees through the lenses is an enriched world overlaid with vibrant, full-color 3D computed imagery,” said Gordon Wetzstein, an associate professor of electrical engineering and an expert in the fast-emerging field of spatial computing. Wetzstein and a team of engineers introduce their device in a new paper in the journal Nature.

Continue Reading.

[meadow-dot-7z]

We made a small game project!! It's a program designed to learn how to play Connect 4, based on the MENACE model. Its algorithm needs some work, so it's a very slow learner, but we're pretty proud of it so far! ^^

[sylphstream]

Sylphstream project announcement

Sylphstream is a first person movement shooter I have in active development. It's been a little while in the making, but I've finally gotten Sylphstream at a point where I feel announcing it!

The github link can be found here!

If you're a modeller, coder, or just interested in following the project at any distance, reach out to me or stick around for more updates!

[chambersevidence]

Search Engines:

Search engines are independent computer systems that read or crawl webpages, documents, information sources, and links of all types accessible on the global network of computers on the planet Earth, the internet. Search engines at their most basic level read every word in every document they know of, and record which documents each word is in so that by searching for a words or set of words you can locate the addresses that relate to documents containing those words. More advanced search engines used more advanced algorithms to sort pages or documents returned as search results in order of likely applicability to the terms searched for, in order. More advanced search engines develop into large language models, or machine learning or artificial intelligence. Machine learning or artificial intelligence or large language models (LLMs) can be run in a virtual machine or shell on a computer and allowed to access all or part of accessible data, as needs dictate.

[codebegins]

No matter, What your background is, You must learn at least one programming language.

[zeemakesthings]

My Introduction

Name: Zee

Pronouns: He/Him

Age: 20

Interests: Gaming, Computers and Electronics, Music, Music Tech - Specifics: Satisfactory, Minecraft, BeamNG, Phantom Forces, Marvel Rivals, Cities Skylines, Subnautica, TLOU, FNAF, LLM, ML, PC Building, HomeAssistant, IoT, Self-Hosting, Automation, Drones, Trains, Photography, House, Jazz, Fusion, Funk, D&B, Sound Engineering, Studio Design, Recording, Mixing, Drumming

Looking forward to meeting new people and sharing my experiences!

[nerdyfangirlingbooks]

Rereading the lunar chronicles but I'm an engineer now and I'm being nitpicky

It took Cinder's scanner 2.6 seconds to recognise Kai?? That feels slow, given how much their technology has progressed? Especially because the system presumably has a LOT of data about him?

It should be able to pick up the points of his face pretty quickly, his disguise was a hoodie (so face not covered at all) and decent current tech can do this in a practically negligible time

And then it just needs to go through the database and I guess it depends how it's doing it. If it's doing a manual searching algorithm then 2.6 seconds is probably actually pretty quick but I assume it would use AI so it should be able to recognise so someone that famous very very quickly

[learn-ai-free]

OpenAI Releases Codex: A Software Agent that Operates in the Cloud and Can Do Many Tasks in Parallel

OpenAI has released a research preview of Codex, a cloud-based software engineering agent that's not just another code completion tool. Codex is a cloud-based software-engineering agent that turns on isolated sandboxes, pulls your repo, and chips away at features, bug fixes, test suites, and even pull-request boilerplates—often in parallel.

What is OpenAI Codex? 📌

→ Cloud-based software engineering agent

→ Can write features, answer codebase questions, run tests, and propose Pull Requests for review

→ Each task runs in its own isolated cloud environment

→ Provides detailed terminal logs, test outputs, and citations

→ Users can create AGENTS.MD files in their repository to instruct Codex on project-specific commands, testing procedures, and coding standards

→ Powered by codex-1

How to use Codex: 📌

→ Users can access Codex through the ChatGPT sidebar

→ Assign coding tasks by typing a prompt

→ Each request is handled independently

→ Codex can read and edit files and run commands like test suites, linters, and type checkers

→ Task completion generally takes between one and thirty minutes

Once done, Codex runs its changes within its sandboxed environment, which users can then review, ask for more changes, open a GitHub PR, or pull the changes into their local setup.

↗️ Full Read: https://aiagent.marktechpost.com/post/openai-releases-codex-a-software-agent-that-operates-in-the-cloud-and-can-do-many-tasks-in-parallel

Codex: Availability 📌

Codex is currently rolling out to ChatGPT Pro, Enterprise, and Team users, with access for Plus and Edu users planned to come soon.