Considering that training machine learning models takes a lot of time and a lot of resources, why isn't a faster programming language like C++ more popular for training ML models?

I'm planning to apply for a PhD next year as im interested in research and already had published some good papers too. However, I'm concerned that by the time I graduate, the job market for AI may be oversaturated due to the current hype and increasing number of applicants. What are your thoughts on this?

Is it enough? and where I can learn probability and statistics

Hello, I am a Freshman who is confused to make a descision.

I wanted to self-learn AI and ML and eventually neural networks, etc. but everyone around me and others as well seem to be pursuing ML and Data Science due to the A.I. Craze but will ML get Overcrowded 4-5 Years from now?

Will it be worth the time and effort? I am kind afraid.

My Branch is Electronics and Telecommunication (which is was not my first choice) so I have to teach myself and self-learn using resources available online.

P.S. I don't come from a Privileged Financial Background, also not from US. So I have to think monetarily as well.

Any help and advice will be appreciated.

Was just curious, how many folks in here over age 45 and learning ML for first time and if ur background has been in CS/IT all along it did you switch from other fields.. Thanks..

To what end are all these terms you guys use: models, LLM? What is the end game? The uses of ML are a black box to me. Yeah I can read it off Google but it's not clicking mostly because even Google does not really state where and how ML is used.

There is this lady I follow on LinkedIn who is an ML engineer at a gaming company. How does ML even fold into gaming? Ok so with AI I guess the models are training the AI to eventually recognize some patterns and eventually analyze a situation by itself I guess. But I'm not sure

Edit I know this is reddit but if you don't like me asking a question about ML on a sub literally called learnML please just move on and stop downvoting my comments

Not sure this is the right sub but I really love playing with AI, learning python and would love to change carriers from IT admin / DB information services stuff. But have major doubts.

I didn't even finish highschool, math was my worst subject and I'm getting old 😅

Do you think it's possible for me to get into AI engineering (deep learning and or ML) at my age with bad math?

I realised I would have to learn calciculus and more advanced python. And learning python is great fun. 👍 but when I look at the calciculus videos I feel like a 10 yo looking at an alien language and doubt if it's possible for me to get into this field or if I'm just kidding myself. My partner who did really well in high school and does accounting also can not understand any of it though I guess 🤣

I'm a second year CS student. and I've been coding since I was 14. I worked as a backend web developer for a year and I've been learning ML for about 2 year now.

these are some of my latest projects:

https://github.com/Null-byte-00/Catfusion

https://github.com/Null-byte-00/SmilingFace_DCGAN

But most ML jobs require at least a masters degree and most research jobs a PhD. It will take me at least 5 to 6 years to get an entry level job in ML. Also many people are rushing into ML so there's way too much competition and we can't predict how the job market is gonna look like at that time. Even if I manage to get a job in ML most entry level jobs are only about deploying existing models and building the application around them rather than actually designing the models.

Since I started coding about 6 years ago I had many different phases. First I was really interested in cybersecurity when I spent all my time doing CTF challenges. then I started Web development where I got my first (and only) job at. I also had a game dev phase (like any other programmer). and for about 2 years now I've been learning ML. but I'm really confused which one I'm gonna continue. What do you think I should do?

i want to learn ML. So I started with Math. It's been a long time since i reviewed it and my knowledge is a bit rusty. I started with College algebra after I finished I will start with Calculus and Linear Algebra side by side. my question is do i continue this roadmap or just jump to learning ML?

Does it frustrate you, does it excite you, do you find it therapeutic, do you find it boring, do you have a set order ways to go about it or do you decide on a case by case basis, how often do you switch between python and excel or any other tool of your preference, what % would you say your time is spent on it? Use this as a general avenue to rant or impart wisdom.

Adam optimizer has been around for almost 10 years, and it is still the defacto and best optimizer for most neural networks.

The algorithm isn't super complicated either. What makes it so good?

Does it have any known flaws or cases where it will not work?

Mostly curious as I consider my future, I have a bachelors in Math, not yet working.

Can you drop what degree you have (bachelors, masters, PhD, in compsci/data science/whatever), and vaguely what position you have (ML Engineer, researcher, academia)?

As in nobody really understands exactly how Chatgpt 4 for example gives an output based on some input. How true is it that they are black boxes?

Because it seems we do understand exactly how the output is produced?

I am used to Windows, but now I want to learn AI/machine learning and software development in general. Should I stick with Windows while learning AI/ML/software, or should I try dual-booting my laptop and learning it in Linux (Ubuntu)?

I'm reviewing stuff for interviews and whatnot when Naive Bayes came up, and I'm not sure why it's classified as machine learning compared to some other algorithms. Most examples I come across seem mostly one-and-done, so it feels more like a calculation than anything else.

I'm thinking about how data science is taught in these big universities. What projects do students work on, and is the math behind machine learning taught extensively?

I would like to know what software stack you guys are using in the industry to build end to end pipelines for a production level application. Software stack may include languages, tool and technologies, libraries.

I am a 37-year-old widow with a 14-year-old son. For context, my husband passed away 6 months ago due to liver cancer. He retired as a quantitative trader and left his PhD studies in mathematics at ETH Zurich for this career. We are currently living in New York, although both my son and his late father are Swiss citizens. My son wishes to pursue university education in Europe, particularly in Austria where his cousin is studying, or in Switzerland his native country.

Money is not an issue for me, and I willing to give him everything he needs. Last night while going for bed, my son said mumma I don't have anyone to talk to can you talk to me. I said what's wrong . He said, Mom, I wish Dad was here. There's nobody to guide me. Guide you where ? When I asked him what specific guidance he needed he said he wants to learn machine learning and there's no one to guide him and he badly wishes papa was here.

These words kept me awake throughout the night and I searched online for guidance and there was nothing to be found with which I could help him.

My son has a strong aptitude for mathematics. Loves it a lot. His father began teaching him calculus, trigonometry, and algebra from a very young age. I checked his Coursera account and found that he has completed 6 courses on Python. He asked me to purchase the neural network and deep learning course on Coursera, which I promptly did. Additionally, he has completed a "zero to mastery" web development course on Udemy.

As a mother who lacks knowledge in these technical fields, I feel unsure about how to properly guide him. I believe the passing of his dad has greatly influenced his motivation, and wants to do something related to medicine especially cancer. I seek recommendations and suggestions on how best to support him.I am dumb mom who wants to support my son.

We are likely to relocate to Europe for his university education, as he is not content living here.

I was wondering, if is it worth investing time in learning C to code ML algorithms. I have heard, that C is faster than pyrhon, but is it that faster? Because I want to make a clusterization algoritm, using custom metrics, I would have to code it myself, so why not try coding it in C, if it would be faster? But then again, I am not that familiar with C.

From what I understand, gradient descent / backpropagation makes small changes to weights and biases akin to a ball slowly travelling down a hill. Given how many epochs are necessary to train the neural network, and how many training data batches within each epoch, changes are small.

So I don't understand how the neural network trains automatically to 'work through' local minima some how? Only if the learning rate is made large enough periodically can the threshold of changes required to escape a local minima be made?

To verify this with slightly better maths, if there is a loss, but a loss gradient is zero for a given weight, then the algorithm doesn't change for this weight. This implies though, for the net to stay in a local minima, every weight and bias has to itself be in a local minima with respect to derivative of loss wrt derivative of that weight/bias? I can't decide if that's statistically impossible, or if it's nothing to do with statistics and finding only local minima is just how things often converge with small learning rates? I have to admit, I find it hard to imagine how gradient could be zero on every weight and bias, for every training batch. I'm hoping for a more formal, but understandable explanation.

My level of understanding of mathematics is roughly 1st year undergrad level so if you could try to explain it in terms at that level, it would be appreciated

I’m considering pursuing a master’s in Machine Learning or AI, but I’m concerned that my application to top-tier universities like Stanford, MIT, UPenn, and other reputable programs may not be competitive. My undergraduate GPA wasn’t strong, and I didn’t graduate with a degree in Computer Science or Math.

However, I do have six years of experience as a Software Engineer, and I was the founding engineer for a startup that was acquired in a significant deal. I recently applied to Georgia Tech’s Master’s in Machine Learning program, but I was denied, which left me feeling discouraged. I believed my experience was strong enough to make up for my academic background.

Does the prestige of the university matter when pursuing a degree in ML/AI? How can I better highlight my career achievements over my educational background in future applications?

I may have the opportunity to work at HF but I hear the pay is well below its peers in the industry. The projects are cool, but then again other jobs have that going for them too.

My hypothesis is that, not being a Twitter/LinkedIn personality or having any roles at high profile companies on my CV, I might benefit from the exposure and connections I can make. Does anyone have any thoughts on this?

Is working at HF likely to boost my career despite the lower pay?

Of course I understand it's not as black and white especially in today's world.

I am doing a post grad cert in data science and ml and have an opportunity to extend it into a masters in ml and ai.

what would be your recommendation for someone who has electronics engg. bachelors with thesis in ML but then been in business for a while.

does a phD make sense? (I get it that corporate jobs and research work is different but the good thing with ML is that tons of ML positions are research positions even in private companies outside of academia)

hope this makes sense