#machine learning software

[ronaldtateblog]

Discover the Best AI Automation Tools for Your Business

Artificial intelligence is revolutionizing the way businesses operate, and choosing the right automation tools is key to unlocking its full potential. According to MIT research, companies that strategically implement AI-driven automation see a significant boost in productivity. For business leaders, the challenge isn’t deciding whether to adopt AI automation tools — it’s determining which tools…

[polyxersystems]

Top 5 Machine Learning Tools for Software Development in 2024

Introduction

Machine learning has been widely used by various industries in 2023. The software development industry can take great advantage of machine learning in 2024 as well.

It has great potential to revolutionize various aspects of software development including task automation, boosting user experience, and easy software development and deployment.

Machine learning could be leveraged throughout the software development process to improve productivity in 2024.

Hence, this blog explores the best machine learning tools that software development industries can adopt for daily development tasks and significantly boost productivity.

But first, let’s discuss the pivotal role of machine learning in software development.

What Is Machine Learning?

Machine learning tools in software development help developers analyze large volumes of data and identify patterns to create more efficient, reliable, and user-friendly software.

In software development, machine learning tools are useful in streamlining workflows, automating manual processes, and generating valuable insights for informed decision-making.

The uses of machine learning tools in software development are wide and growing. Let’s explore some of its real-life examples to understand more.

Top 5 Real-World Machine Learning Examples

1. Recommendation systems

This is one of the most famous applications of machine learning. Product recommendations are commonly used and featured by businesses.

Using machine learning, developers can build software that can track user behavior to recognize patterns through their browsing history, previous purchases, and other shopping activities. This collection of data helps in predicting user preferences.

Various companies like Spotify and Netflix use machine learning algorithms to recommend music and shows to their customers based on their previous listening and viewing history.

2. Social media connections

Another most popular machine learning algorithm is the “people you may know” feature on social media platforms like Instagram, Facebook, LinkedIn, and X.

According to user contacts, comments, likes, or existing connections, this machine-learning algorithm suggests familiar accounts that users might want to follow or connect with.

Read More: Best Machine Learning Tools

[zooplekochi]

They call it "Cost optimization to navigate crises"

[cyle]

still confused how to make any of these LLMs useful to me.

while my daughter was napping, i downloaded lm studio and got a dozen of the most popular open source LLMs running on my PC, and they work great with very low latency, but i can't come up with anything to do with them but make boring toy scripts to do stupid shit.

as a test, i fed deepseek r1, llama 3.2, and mistral-small a big spreadsheet of data we've been collecting about my newborn daughter (all of this locally, not transmitting anything off my computer, because i don't want anybody with that data except, y'know, doctors) to see how it compared with several real doctors' advice and prognoses. all of the LLMs suggestions were between generically correct and hilariously wrong. alarmingly wrong in some cases, but usually ending with the suggestion to "consult a medical professional" -- yeah, duh. pretty much no better than old school unreliable WebMD.

then i tried doing some prompt engineering to punch up some of my writing, and everything ended up sounding like it was written by an LLM. i don't get why anybody wants this. i can tell that LLM feel, and i think a lot of people can now, given the horrible sales emails i get every day that sound like they were "punched up" by an LLM. it's got a stink to it. maybe we'll all get used to it; i bet most non-tech people have no clue.

i may write a small script to try to tag some of my blogs' posts for me, because i'm really bad at doing so, but i have very little faith in the open source vision LLMs' ability to classify images. it'll probably not work how i hope. that still feels like something you gotta pay for to get good results.

all of this keeps making me think of ffmpeg. a super cool, tiny, useful program that is very extensible and great at performing a certain task: transcoding media. it used to be horribly annoying to transcode media, and then ffmpeg came along and made it all stupidly simple overnight, but nobody noticed. there was no industry bubble around it.

LLMs feel like they're competing for a space that ubiquitous and useful that we'll take for granted today like ffmpeg. they just haven't fully grasped and appreciated that smallness yet. there isn't money to be made here.

[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.

[the-epilogue]

first digital drawing since I took a break from digital art

[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.

[serialport0]

"If we keep on optimizing the proxy objective, even after our goal stops improving, something more worrying happens. The goal often starts getting worse, even as our proxy objective continues to improve. Not just a little bit worse either — often the goal will diverge towards infinity. This is an extremely general phenomenon in machine learning. It mostly doesn't matter what our goal and proxy are, or what model architecture we use. If we are very efficient at optimizing a proxy, then we make the thing it is a proxy for grow worse."

Too much efficiency makes everything worse: overfitting and the strong version of Goodhart's law

[decamarks]

I hate to talk about other people's Tumblr posts, but,

Please learn what words mean. [1]

[softwaredevelopmenthub25]

Explore the innovative software development services offered by Software Development Hub (SDH). From MVP development and AI-powered solutions to ERP software, IoT, and cloud migration, SDH delivers cutting-edge expertise for startups and businesses worldwide. Discover insights, project highlights, and tips on building user-centric applications and driving digital transformation.

[gamesandgems]

I'm makin' band sew-on patches with an embroidery digitizing software. And gettin' squirrely with the fabric i'm picking as the background. Also ran out of black thread so ended up with a summer-vibe patch using dark teal thread on an almost sun-print batik fabric.

Band is Indeyevid from NJ, if you're lookin for some punk garage band jams. They recently made a music video for their song Red Dye 40.

Lessons learned on embroidery n patches:

1) (and this is so silly fundamental it's almost not worth saying,) but when you hoop stabilizer or your fabric, dear gods make sure the piece is larger than your hoop by at least an inch on all sides. 🥲 i had to crossways my washable stabilizer and effectively use twice as much for the patches i did make because i got a tube that was too narrow for my hoop.

2) thiicccc boarders on your patch make it easier to hide the raw edge that gets covered by the satin stitch boarder. 0.18" for this size patch, up to 0.25" on really big patches.

3) duh to me, use the preview stitch feature of the program to make sure you dont accidentally hit outline on a design part you didnt want outlined. ...And then make a patch that looks so misaligned that it doesnt pass your personal preference.

4) basic quilt cotton, - quite a flimsy choice for patches, needs heavy tear away stabilizer for support to make it hearty. In future, look for twill, or canvas for patch base (save on needing to layer materials).

[marshroom580]

"AI" this and "AI" that, NONE OF IT IS INTELLIGENT, WE HAVE NOT MADE INTELLIGENCE YET, WHY ARE WE LYING, DO WORDS MEAN NOTHING

[codebegins]

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

[abathurofficial]

Abathur

At Abathur, we believe technology should empower, not complicate.

Our mission is to provide seamless, scalable, and secure solutions for businesses of all sizes. With a team of experts specializing in various tech domains, we ensure our clients stay ahead in an ever-evolving digital landscape.

Why Choose Us? Expert-Led Innovation – Our team is built on experience and expertise. Security First Approach – Cybersecurity is embedded in all our solutions. Scalable & Future-Proof – We design solutions that grow with you. Client-Centric Focus – Your success is our priority.

[nixcraft]