u/Right_Nuh

I have been wanting to buy a PC for a long time but the price is really high. I prioritized buying a tablet 2 years ago then last year my laptop broke and I had to buy a new one. But I need a computer so I can give my laptop a rest and because some of the things I work with require high power. I am a grad student in CS and I don't know anyone who doesn't a PC. I found an expensive PC as outlet PC for $850 (it checks almost all requirements I want but the fact that it is an outlet is bothering me even tho it is according to the retail new with original package missing and I have a 2 years warranty). I live on my own so I have pay bills but since high school I have worked mulitple jobs (uni is free where I live) that I have saved enough to not an economic catastrophe. I really wanted to travel this summer and it didn't work out. The point is that I cannot justify it to my own head. I don't really need it, but it is good to have and I would be happier and I have been wanting it for years now. What do you all think?

So, I’m building this app as a personal project, but I really want to get it hosted. It won’t have much traffic, since it won’t really be used unless employees or other people I send my portfolio to decide to check it out. I don’t mind database size limits either, I just want to get it up and running.

The last semester of my masters degree, I need to do a research degree project. The project is supposed to be done at companies or research groups. With that said almost all require cover letter but I have no idea how to write one for a research applicaiton. So I am just trying to prepare a template. Anyways, I have no actual project or experience outside of school and a few unimpressing personal projects that I don't think would be interesting for research related things. What could I someone that has never worked on real project have to offer? Assuming the paragraph sectioning is good, what part needs work? Do you think I should mention speicific project in paragraph 3 or is it good the way it is. My plan is that only paragraph 2 will be changed based on the application and a few edits on paragraph 3.

>Paragraph 1: Introduction
I am writing to express my interest in exploring master's thesis opportunities at [Company Name]. I am currently pursuing a Master's degree in Computer Science with a specialization in Cognitive Systems at [university name], where I have developed a foundation in machine learning, programming, and data analysis. I am reaching out to explore whether there may be an opportunity to complete my thesis within your team.

>Paragraph 2: Why this company? 
I am particularly interested in your work on [specific task], as it addresses challenges in [field]. I am excited by the opportunity to contribute to research in this field through a master's thesis project while applying my background in [relevant skills] and learning from your team's expertise. 

>Paragraph 3: Why am I a good fit?
Through my studies in Computer Science, I have developed a strong foundation in programming, machine learning and deep learning. I have gained experience with several programming languages, particularly Python, and have applied these skills through academic projects involving biometric identification, large language models (LLMs), and software development. I believe my analytical mindset, programming background, and willingness to learn would allow me to contribute effectively to your research project.

I am trying to import torch and other modules from langchain but it takes like 2 min to import them. It worked yesterday to import and execute one module in reasonanle amount of time. I turned of my windows firewall to see if it was for some reasons scanning them but nothing is changing. Any tip?

I am learning LLM but right now for my individual project I just wanted to fine tune pretrained models like like gpt2, t5-small or similar models for chatbot or simple conversations. I don't even know if it is a good idea because the result I got was not good. I was able to chat with it but the responses were horrible. A reason can be because my dataset is also not great but does anyone have a recommendation on datasets and how to approach this?

A recommendation to use a model that has a very large number of parameters won't be feasible for me at the moment. I also don't have a lot of time to make my own datasets.

An experiment that was carried out 80 times resulted in the following mean 9.7632175 and std = 0. 610496. The same experiment was repeated by another research group, who obtained the following std = 0.79510083825497 μ2= 9.6394725.

Are the results or the mean values consistent with each other, or is there a statistically significant difference? Express your answer in terms of a p-value.

My answer:

Let the null hypothesis (H0) be that the mean from a and from b are equal: 
μ1 = μ2.
Let the alternative hypothesis (H1) be that the mean: μ1 ≠ μ2.

If the null hypothesis is true, what is the probability of getting a sample with a statistic that gets? If that probability is lower than the significance level alpha AKA 5% then we reject the null hypothesis.

I am not really sure how to continue after this. I found a similar problem online and followed a little but got lost, I calculated the standrad error to be 0.11207. Then I calculated the t-value to be 1.104. Am I mistaken? How should I contuinue?

I have so far only experience with deep learning in the context of LLM and voice recognition related tasks. But I wanted to do one project for my portfolio about being able to control the cursor on the computer using eye gaze. I am not looking for state of the art performance, just something that works enough. I did find other machine learning approahces like importing trained models like "haarcascade_eye" using opencv. I also started to discover pretrained models like L2CS-NET but it requires different versions of python libraries and I am also a bit new to python so I don't know if it is worth it. I wasted hours yesterday trying to fix dependency issues.

With that said, I don't think I have the datasets or time or the knowledge enough to create a complicated model from scratch, I was looking to fine-tune already existing models. And I would apprecitate a little guidance. If I should abandon the deep learning aspect, I will also do that if there is a better way.

I’m stuck on a philosophy of science / logic assignment question and could use some input. Here is the scenario:

A group of students is randomly assigned to two groups to solve a coding problem. Group C uses Haskell (functional) and Group D uses Java (imperative language). The fastest programs end up coming from the Java group.

The Question: Can we draw the conclusion that functional languages give slower code?

The answer is no because of the students' prior experience with Java which is a much more popular language than haskell. But I'm trying to figure out the exact framework to prove it based on my course, and I keep hitting a wall with each one we tried:

Reichenbach’s Principle (Common Cause): If we say A = haskell, B = slow program, and C = "prior knowlegde in imperative languages like Java since they are much more popular". In this case if I argue for a common cause C, then C doesn't really cause A. C can cause B but to use this model I guess it must cause both.

Hempel’s DN Model: We don’t have a well-accepted law stating functional languages are slower, so a DN model feels irrelevant.

INUS Condition: Haskell doesn't feel like a necessary part of an unnecessary but sufficient condition here, because inexperience alone can make any language slow.

Am I looking at these the wrong way? Is there a better way to prove this?

I just took a deep learning courses in LLM related things and I want to become better at it but I don't know where to start. What is an intermediate level project that isn't too easy or too hard to do but looks good on resumé. I am mostly doing this to learn but also want to build up my portfolio. I also want to focus on fine-tuning already existing models right now because that is one thing I want to be better. But I am open for anything from creating a model from scratch as long as there is good amount of datasets for it. Right now I am thinking of doing a chatbot of some kind or recipe creator given fridge items. I am open for suggestions and a little description. Thanks beforehand.

Hi guys so I experimenting on using pretrained models to predict age and gender using sound/voice. After searching for free datasets for days I only found one that fulfills almost all my requirements and that was large and free. But unfortunately that dataset was labeled as [teen, twenies, thirties, ...,]. And the older they get I have less data. I limited my dataset and to balance it but I still have like 3-5 dataset for male in 90s and nothing for a female. So the problem here is I don't have actual ages of the people and I have no data set below 10 and above 90. The papers I was reading and taking inspirations from never specify what they did other than that the treated age like a regression problem between 0-1 but how am I supposed to do that when my range is 0.1-0.9

I am working with deep learning and my dataset has only people starting from teenage. Then after 70, I basically have no data, especially for women. For both male and female I have no data for 90s and few for 80s.

class NeuralNetwork(torch.nn.Module):
    def __init__(self, num_of_gender_labels = 2, input_dim = 768):
        super().__init__()
        self.shared  = nn.Linear(input_dim, 1024)

        self.age = nn.Linear(1024, 1)

        self.gender = nn.Linear(1024, num_of_gender_labels)

Everything I have done so far requires that I use a single head for both gender and age. The paper I was reading only mentions that I should treat gender as a classification problem and age as a regression. What do I do because the MAE for age is high. If anyone else has a better dataset that I can get my hands on quickly like this I would appreciate it. I am using commonvoice.

I am new to deep learning and so far I have not dealt with anything like this. I have a 30GB dataset. I am trying to filter it preparing it for training but it is taking a lot of time, I mean it would take like 40h at this rate to finish extracting features. I have access to a remote GPU through my school but uploading the 32GB there has been a pain in the a** and I don't even know if I am even supposed to do that. Eitherway I have no idea how to deal with this. Does anyone have a tip or a suggestion?