How It Works:

1. Join the 👉Discord of 300+ Members
2. PayPal for buyer protection.
3. I'll send you a link like this to redeem:

https://www.perplexity.ai/join/p/priority?code=PPLXO2I5Y85QOW |

Check out the vouches from buyers.

Have a double BSc in CS and maths, now doing an MSc in machine learning, studied hard for these degrees, enjoyed every minute of it, but am now waking up to the fact that the few job openings that do seem to be there in Data Science/MLE seem to involve building systems that just call the API of an LLM vendor, which really sours my perspective. Like: that is not what I went to school for, and is something almost anyone can do. This does not require all the skills I love and sunk hours into learning

Is there anything I should specialize in now that i'm still in school to increase my chances of getting to work with actual modelling, or is that just a pipe dream? Any fields that require complex modelling that are resistant to this LLM craze.

I am considering doing a PhD in ML, but for some reason that feels like a detour to just becoming another LLM api guy. Like, if my PhD topic does not have wider application, when I finish the PhD all the jobs available to me will still be LLM nonsense.

It seems nowadays the model people are using is RVC V2 which was release back in 2023. It’s extremely shocking to me that this still is what people are using considering its well over a year old.

Have most people missed something or is progress on this just incredibly slow?

A lot of people have been asking to see how a more senior ML engineer would approach an ML system design interview. So, I decided to tackle a classic problem: designing YouTube’s recommender system.

I recorded this in one take to keep it as realistic as possible—no rehearsals, just my raw thought process. It’s not perfect, and I’ve added a ton of notes on what could have been done better. My goal was to show the kind of thinking and trade-offs a Staff MLE (former FAANG) might demonstrate in an interview setting.

There’s not a lot of content out there showing this level of detail, and I hope it helps those preparing for ML system design interviews or just curious about how these challenges are approached.

I’m still new to YouTube, so please be kind! Would love to hear your thoughts or questions—happy to share what I’ve learned from both sides of the table (candidate and interviewer).

Check it out 👉 https://youtu.be/9U48NlbOzCU

a cool idea I have, a tor based crawler machine learning model that people could use to say search there email, only problem it could be used to find others emails and stuff they are not there own but mabey there would be a solution such as email authentication to prevent this. It would use Scrapy or Beautiful Soup or scraping could use PyCryptodome for encryption. Would anyone like to help me on this project? I think it could be a useful tool made open source and put a link for donations to make and train better versions in the future. If your not interested in joining but have good ideas or advice it would be much appreciated.  One last thing I have a few ideas for but needs to be figured out is instead of having to input "example.onion" how could we make the tool search the a large but specific portion dark web automatically theres a few thing I have in mind to accomplish thisi let me know if you are interested in discussing further

I have a super high quality, legal, licensed voice dataset. I am wondering:

- 1. How much compute ($) would it cost to train a model like ElevenLabs?
- 2. How many hours of voice data would you need?

I have a budget, but I'm trying to work out if it is ballpark enough or if I am way off.

Anyone who's had experience in this area - would love to hear from you!

Thanks so much,
Josh

Hi everyone,

I know this type of question might be cyclical, but I’m genuinely looking for up-to-date advice on the best online degree program for AI, specifically for someone like me who doesn’t have a technical background.

I started by learning Power Automate at work, which led me to explore Azure and OpenAI for creating solutions like text analytics and copilots, even though I don't have coding or math background, I don’t even have a formal degree, but those projects boosted my confidence and interest in the field.

My ultimate goal is to transition into an AI-focused role—like AI engineer. With a title like a degree, I’d have the skills and credentials to move into a dedicated role in this area in my actual work or to move somewhere.

I’ve been considering IU University’s programs, but unfortunately, they haven’t been replying to my inquiries.

If anyone has suggestions, I’d love to hear from you!

Thanks in advance for your help!

Hi, there hope your doing well.

So, how do you overcome the situation where by you need to create ML project so that you can put it to your resume… and ending up doing nothing Despite of knowing in and out ML algorithms ?

Maybe what do you do ?

Thanks.

Which program did you/are you attending? How was/is it? How job-ready do you feel? What were its strengths and weaknesses?

I'm in my freshman year of a bachelor's in Mechatronics, and I've been working on a few implementations of CNNs in Robotics, one of which was was Image Classification, and the other in a self-driving car project.

When I started these projects a couple months ago, I was told to immediately go through some courses on Coursera on Image Classification and Autonomous navigation, and while I did finish them and manage to implement CNNs in the aforementioned projects to at least some rudimentary degree, I feel like I've been missing out by skipping all the math behind CNNs and Machine Learning Models in general, especially because I find the ML side of Robotics very fascinating and am considering to go down the path of academic research in the field down the line.

I've only taken introductory-level classes on Calculus, Numerical Methods and Linear Algebra, and I don't really want to rely on College to teach me the math required, so I've decided to take a self-directed approach to learning. What kind of math do I need to know to understand ML? How can I test my understanding of the math that I learn along the way, and what resources should I follow to learn it all?

Thank you.

The thing is I am curious to learn abt AI and ML I don't have any clue about it. I learnt python and some libraries like numpy and some pandas idk what to do now. Any guidance is highly appreciated 🙂

i was arts student i've done BCA and now i am in the last year of MCA general. I want to go in AI/ML field. Now i am looking for internships in AI/ML. How can i break into this. suggest me

I have an internship interview next week and I need to spend 5-10 minutes presenting something I've been reading about recently. Planning on doing machine learning, but I'm no expert yet, what should I talk about? Its with a head of HR so she probably wont have a clue what I'm talking about

Thanks, Ryan

I have two predictors (x) A and B with the goal to predict target (y) C. My inputs are 2D combinations of time series using 60 minutes time history of A and 30 minutes of B.

Assume we are predicting C at time t = 0, then the input is as follows:

Input_0 = [A(t=-60), A(t=-59) ... A(t=0), B(t=-30), B(t=-29) ... B(t=0)]

Then for t = 1,

Input_1 = [A(t=-59), A(t=-58) ... A(t=1), B(t=-29), B(t=-28) ... B(t=1)]

As you can see, inputs 0 and 1 have overlapping time history.

My question comes from the test/train/validation splitting. Usually, one would want to shuffle the on the test/train/split, but in my case this could mean having input 1 in the test and input 2 in the training. My current splitting code looks like this, based on sklearn train_test_split

x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=test_split, random_state=seed, shuffle=True)

x_val, x_test, y_val, y_test = train_test_split(x_test, y_test, test_size=val_split, random_state=seed, shuffle=True)

This will clearly cause data leakage between the data sets, and has been showing on my validation and test loss plots.

How can I effectively split these inputs? Do I turn off shuffle in train_test_split and then shuffle the data afterward? What about when breaking up the test set into test and validation?

I saw sklearn's TimesSeriesSplit, but that seems to be something that is done before my input arrays are configured.

Thank you!

I've seen this course recommended on this subreddit a lot, but I'm a bit confused

The materials, slides and assignments I found online are from 2024. However, the most recent video playlist I could find is from 2017, so the slides and topics often don't match, especially for the later parts. Which one should I use? My guess is I should go with the 2024 one since it's more recent, but I've no idea if the available materials enough. For the later sections, there are only slides from the lectures, no detailed pages, so I’m worried I might struggle without the lecture videos

Am I missing something?

Нещодавно зрозумів, що краще і цікавіше вчитися не просто за документаціями, а на реальних проектах, де теоритичні знання будуть підкріплюватись практикою. Мені цікаво, які проекти можна було б зробити, щоб освоїтись в темі машинного навчання, і бажано, знайти роботу, бо тема реально цікава

Hi,

I'm applying transfer learning with resnet50 to a set of 7k images. The images come from an experiment in physics and they are basically a black background with 4/5 parabolas in the middle with a white spot at the bottom. What I want to predict is the distance between the center of the white spot and the beginning of the first parabola, i.e., the exact pixel row in which a parabola exists for the first time. To add more context, the parabola of interest is always at the right side of the image and most of the time the other parabolas "begin" to exist before it (i.e., their first pixels are located closer to the bottom than the first parabola).

Fortunately, I have a set of labels with the exact row in which each of the pixels from the first parabola begins to exist so I thought a CNN approach would be sufficient with a regression task would be enough but it's not the case. I loaded the images in 224,224,3 format, normalize them and I also normalized the labels too with a scaler. Then I applied the model with all the layers frozen, added data augmentation to flip them horizontally inside the model, added a dropout layer and a dense layer with only 1 output since I want to do regression and it doesn't work.

My tries? Many, I have played around with the learning rate and the dropout factor, with the denormalization of the labels, with using a different batch size (16 instead of 3), including the data augmentation layer as I mentioned before or unfreezing the last 4/5 layers of ResNet and still, no improvements. Everytime the Mae and the validation losses get stuck around the same values, for example, the mae doesn't decrease of 0.50 when the values are normalized so that means an error of 50%.

TLDR; my CNN to do regression from a set of images with labels doesn't learn. It gets stuck always in the same value of Mae and I have already tried to change many hyperparameters or even unfreeze a few layers and it always delivers the same results

Hi all - First, this is a great forum. I've been lurking here for awhile reading the different posts about NLP v LLM. I can see great potential in NLP in transforming productivity in my profession. However, as a newbie, I am seeking some ideas for NLP projects that I can build. I want to eventually be well-versed in LLM too but as many of you pointed out, LLM is a 'cruise missile' and while it can replace NLP, it may be unnecessary for my purpose here. My question is: are there companies out there still building NLP tools instead of leaning heavily on LLM as the default? Say, you want to build a sentiment analyzer in a specific domain, what are the advantages and limitations of NLP as opposed to turning to LLM for such an analyzer?

In other words, is NLP still relevant in an era when LLMs are proliferating? Thank you!

Hello all, I am facing a problem packaging my code into an executable using Pyinstaller/autopytoexe.

My code uses easyocr to do some tasks, when I try to package it, the final executable terminates without any errors.

After hours of debugging and trying, I found out that the problem lies in the line "import easyocr"

nothing after this line gets executed.

It seems like they need the students to have knowledge on stats, after watching the first 2 video on the course they immediately jump on the quiz that I have no idea how to answer (unless I ask ChatGPT to explain which will take some time), still I think I wouldn't be able to understand all the lectures if I have no prior knowledge on Statistics. Should I study stats or straight up ask chat gpt?

Hi.

The company I work for will provide a ML course for me. I'm a software engineer with several YOE and have implemented some basic CNN models in the past. We work with structured data like camera images, so I was thinking of taking the Deep Learning nanodegree at Udacity. However, I am afraid of losing this precious time at something too basic or slow.

I would you like to know your experiences and what you would recommend. I've seen posts in the sub, but many responses suggested one could get similar or better results with free material. Since I'll be doing it during work hours I would like to be able to show my company a conclusion proof, that's why a structured course would be preferred over self-learning. I'm open to hear free suggestions as well, as long as it fullfil this requirement.

So we are working on a clge project on TMDB Dataset from kaggle

https://www.kaggle.com/datasets/asaniczka/tmdb-movies-dataset-2023-930k-movies

We are trying to do regression. But the problem is this data contains mostly 0's I mean 75% of columns have zero values in it.

I need your help for performing this model either it is regression or classification. But we werent taught NLP yet so we will be working on ML (supervised and unsupervised - clustering).

datatypes

columns

there are more than 75% records with zeros in revenue, budget, vote columns

So I need your help in performing this project. Or am I missing something I can fill them with Mean but it wont fit with the context of the data. I need your help. You can dm me as well

Thanks

I have an approximation of posteriors which is in the form of the image, where, F, S and Q are well defined real numerical tensors. 

approximation

I am looking for a way to do fast sampling on this approximation (by fast I mean  t< 1 second), I thought that maybe normalizing flows could be a good idea because the functional form of the log posterior is well defined. However, before I try anything I wanted to hear opinions of people who actually use normalizing flows, to see if it is worth spending time on that. As I said I want this to be sampled really fast (t< 1 s) and I am aiming at a 11-dimensional parameter space.

Hey,

In my company, there is a project on dating. I have been tasked with creating a recommendation system based on user behavior and interactions to suggest users according to their interests. I need guidance and hope someone can help me.

I have been doing research in CS NLP and submitted paper to ARR Dec cycle for first time and checked the box to get anonymous preprint. How long till ARR gives me url of anonymous preprint or do I have to upload it myself to ARR preprint server?