BeNN
I should've started C long time ago! It's SUPERIOR🔥
The funniest part is I found C more easier to grasp and straight forward than any other language... C is so GOATED
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Forwarded from EdueraSkills
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After a lot of students DM’d us saying “the price is a bit high for us”, we listened. ❤️
Because our true goal has always been to enlighten as many students and people as possible, we’ve decided to take the risk and make our courses even more accessible!
💥 For a limited time, we’re offering up to 50% OFF across all our courses 👇
🔰Python Programming – from 800 birr to 500 birr
🔰Machine Learning – from 1000 birr to 550 birr
🔰 Python Libraries (NumPy, Pandas, Matplotlib, Seaborn) – from 800 birr to 400 birr
🔰 Introduction to Computer and Technology – from 1000 birr to 420 birr
🎯 Learn real, job-ready skills at a fair price - made for you.
🌐 Visit EdueraSkills.com and grab this offer while it lasts!
#EdueraSkills #SkillUp #Python #MachineLearning #DataScience #Technology #Ethiopia #OnlineLearning
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EdueraSkills
🎉 Big Update from EdueraSkills (EdueraSkills.com)! – Up to 50% OFF 🚀 After a lot of students DM’d us saying “the price is a bit high for us”, we listened. ❤️ Because our true goal has always been to enlighten as many students and people as possible, we’ve…
I have heard many people saying it's hard for student to pay 1k and lower it.... Here's it then and actually the profit will be super lower and may even be negative 😭 15% tax, payment fee, cloud billing most of all!
Enjoy it.
Enjoy it.
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Which method is better for finding parameters in Linear Regression?
Anonymous Quiz
55%
Gradient Descent
26%
Normal Equation
19%
Moore–Penrose pseudoinverse
BeNN
Which method is better for finding parameters in Linear Regression?
Most people said GD, but Gradient Descent is like trial and error: it keeps adjusting the parameters step-by-step until it finds the best fit. It works great for very large datasets, but it takes time, needs a good learning rate, and might not always land exactly on the true optimal solution.
The Normal Equation, on the other hand, directly gives you the best parameters in one formula — but only if your data matrix behaves nicely (that is, if X^TX can be inverted). When it can’t, the Normal Equation simply breaks down.
That’s where the Moore–Penrose Pseudoinverse shines. It’s like an upgraded version of the Normal Equation that never fails — even if your data has redundant features, missing rank, or weird shapes. Instead of forcing an inverse, it uses Singular Value Decomposition (SVD), which is a little bit sophisticated math in Linear Algebra😁, to find the most stable and mathematically perfect least-squares solution possible. In simple terms, it always works, it’s more accurate, and it’s mathematically elegant.
The Normal Equation, on the other hand, directly gives you the best parameters in one formula — but only if your data matrix behaves nicely (that is, if X^TX can be inverted). When it can’t, the Normal Equation simply breaks down.
That’s where the Moore–Penrose Pseudoinverse shines. It’s like an upgraded version of the Normal Equation that never fails — even if your data has redundant features, missing rank, or weird shapes. Instead of forcing an inverse, it uses Singular Value Decomposition (SVD), which is a little bit sophisticated math in Linear Algebra😁, to find the most stable and mathematically perfect least-squares solution possible. In simple terms, it always works, it’s more accurate, and it’s mathematically elegant.
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BeNN
Most people said GD, but Gradient Descent is like trial and error: it keeps adjusting the parameters step-by-step until it finds the best fit. It works great for very large datasets, but it takes time, needs a good learning rate, and might not always land…
Note: This is for LInear Regression alone not for all of algorithms. By the way, when you call model.fit() in scikit-learn it uses PseudoInverse under the hood to find optimal parameters and that is why you don't have to specify like lr, n_iters and etc.
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All my Linkedin is filled with feed of people graduating from INSA and I just remembered sth. I'm not sure if it's good idea to talk about it, but since they ain't CIA i'll share ya one story..... The thing is I and My friend had opportunity to present our work when we were at Grade 12 in one Inauguration ceremony. I built chatbot with local language + English and I had opportunity to present to Prime Minister and he was amazed and the best thing was he truly understands AI and it was simple to explain.
The thing is at that moment The INSA director was telling to one guy to contact me and after he took assignment from her he comes on and asked me to build sth I'm not gonna talk here😁(got sound Spy movie), which is really scary and NSA shit honestly and offered me a job to work there as AI Engineer at grade 12 starting and I asked him some question and his response was really scary and The surveillance they do on people is pretty scary!! I surely declined the job and I remembered that I declined job offer from them 🤣I sometimes wonder the outcome if I have accepted it.
The thing is at that moment The INSA director was telling to one guy to contact me and after he took assignment from her he comes on and asked me to build sth I'm not gonna talk here😁(got sound Spy movie), which is really scary and NSA shit honestly and offered me a job to work there as AI Engineer at grade 12 starting and I asked him some question and his response was really scary and The surveillance they do on people is pretty scary!! I surely declined the job and I remembered that I declined job offer from them 🤣I sometimes wonder the outcome if I have accepted it.
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with all due respect please If your knowledge is limited to GenAI or any of AI subfields don't call yourself AI Engineer🤨It is really Harassment for those who spent more than decades and master a lot of subfields and then could call themselves AI Engineer.
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BeNN
"Mathematics is the language with which God has written the universe." Galileo Galilei
Math was one of the main reason I changed my focus from web dev and app dev to AI. All the magic thing AI is doing today is basically a math! It is just ELEGANT
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I have spent around 60 hours being productive this week and Surprisingly I'm disappointed😭🤣....i know it is almost 9 productive hours per day, but there was a time I had done around 100 hours per week ~ 14 hours per day and was hoping to get near that.
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Forwarded from The Data Guy
Hey, i’m Simon 👋
people kept saying “ask the data guy”—so here i am.
I’ve worked across the stack: Data scientist → Data engineer → Data analyst/Business analyst.
translation: Trying to make sense of that crazy data you always avoid
Why this channel
i couldn’t find many channels for our kinda people—practical, curious, no ego.
so… here goes the channel. a place to learn, swap notes, and grow together.
the only one i know is @neural_netss 🔥
What this is
a community for ai, data, and research enthusiasts. a chill corner for anyone who wants to learn.
we’ll focus on learning. small, clear lessons. shared notes. tiny projects we can finish. readable code. quick feedback. progress over polish.
What we’ll do
- quick explainers (plain english, real examples)
- build-in-public: notebooks, dashboards, pipelines
- tool showdowns (pros/cons, not hype)
- lightning q&as + office hours
- quiet drops: roles, collabs, founder-friendly pilots
Who can join
anybody who is interested in data+ai , data folks (all levels), ai/ml engineers, researchers, product/ops, students, and the data-curious.
ai founders + big-company leaders, pull up a chair to meet talent, swap notes, and spot opportunities early.
Low-key benefits
faster answers, vetted resources, honest feedback, early looks at gigs/collabs—and smart people who actually reply.
House vibe
curious > perfect • share what you know • no spam • respect each other and their craft
Drop a 👋 with where you’re based + what you’re building/learning next.
lurkers welcome—jump in when you’re ready.
Let’s make sense of this data🚀
@datawithsimon
people kept saying “ask the data guy”—so here i am.
I’ve worked across the stack: Data scientist → Data engineer → Data analyst/Business analyst.
translation: Trying to make sense of that crazy data you always avoid
Why this channel
i couldn’t find many channels for our kinda people—practical, curious, no ego.
so… here goes the channel. a place to learn, swap notes, and grow together.
the only one i know is @neural_netss 🔥
What this is
a community for ai, data, and research enthusiasts. a chill corner for anyone who wants to learn.
we’ll focus on learning. small, clear lessons. shared notes. tiny projects we can finish. readable code. quick feedback. progress over polish.
What we’ll do
- quick explainers (plain english, real examples)
- build-in-public: notebooks, dashboards, pipelines
- tool showdowns (pros/cons, not hype)
- lightning q&as + office hours
- quiet drops: roles, collabs, founder-friendly pilots
Who can join
anybody who is interested in data+ai , data folks (all levels), ai/ml engineers, researchers, product/ops, students, and the data-curious.
ai founders + big-company leaders, pull up a chair to meet talent, swap notes, and spot opportunities early.
Low-key benefits
faster answers, vetted resources, honest feedback, early looks at gigs/collabs—and smart people who actually reply.
House vibe
curious > perfect • share what you know • no spam • respect each other and their craft
Drop a 👋 with where you’re based + what you’re building/learning next.
lurkers welcome—jump in when you’re ready.
Let’s make sense of this data
@datawithsimon
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The Data Guy
Hey, i’m Simon 👋 people kept saying “ask the data guy”—so here i am. I’ve worked across the stack: Data scientist → Data engineer → Data analyst/Business analyst. translation: Trying to make sense of that crazy data you always avoid Why this channel i couldn’t…
Check out this channel to learn more about Data Science.
BeNN
Anyone wondering Which part of math to cover to master ML.. I recommend Linear Algebra and Calculus (all three levels from I to III). Then You are good to go. @BeNN_Pi
Most of all don't let urself down because you have low Mark or GPA on those subjects in ur highschool or uni.... The main thing is understanding what's going on and how it works... The uni and highschool teachers focus more on how to make it complex with adding stuff into it and let them get ❌ than actually teaching what it's.... So the important thing is U understanding the topic and applying it not just having great GPA at those areas.
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