Submitted by AutoModerator t3_ybjvk5 in MachineLearning
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Thanks to everyone for answering questions in the previous thread!
- It takes a lot of time. (model selection x HPO x test/validation)
- The most important part of a data project is data preparation/feature engineering, modeling is somehow the simplest part.
- IMO it takes much longer to use a visual interface than scripting.
Thank you for sharing this with me
Been seeing a lot of diffusion work lately, and I want to understand this topic. Which research paper(s) should I start reading to get into difussion learning?
I would start with the illustrated stable diffusion for a high level overview. Then I would suggest reading the annotated diffusion model which goes over implementing the original diffusion paper by Ho et al.
Hi, I'm learning how to use GPT-J-6B. I am wondering if there is a way to do something like autocompletion with it? Based on input, generate a set of expected next words with probabilities?
I see many tutorials, but they all use it to just generate lots of text. But I'm looking for it to generate a tree, not just the DFS path. If someone can help point me to a specific function in the codebase, I would appreciate it.
i want to make a game using voice as main way to play it. In game the player will be given a word to say, how do I calculate the percentage similarity of pronunciation in the dataset that I have and the pronunciation of the player ? what's the lightest and best methode ?
Apologies if this isn’t a right question for this sbrdt.
Title - Things to have on resume for first co-op
I am a third year Math&Stat undergraduate, previously graduated with diploma in CS and two year web dev experience.
I want to eventually get into ML. I plan to do my first work term in summer 2023. What things (programming languages/certs/courses/etc) should I learn which will give me an upper hand? I understand that ML is very specialized field and I may not be able to find an ML undergrad coop job, is there a position(s) you could recommend which can serve as a foundation and eventually lead me into ML?
I do really appreciate any feedback
I would recommend Andrew Ng's courses on Coursera. He is very respected both as a researcher and a teacher of ML. The courses start from the basics with linear regression and evolve to treating neural nets and deep learning. With your education, you'll have no problems with the mathematics: matrix theory, multi-dimensional calculus (in particular gradient flow) and some probability theory. He explains the intuition behind many of these topics very well, though, but it makes it easier to already be familiar with them. As for programming languages, the assignments for his first course on ML were in Octave, if I remember correctly, but he later switches by Python, which is by now probably the number one language for these purposes due the the multitude of libraries for ML. As you have a diploma in CS, I assume that you are already fluent in some programming languages, and it would be a good exercise to build your own ML model, e.g. neural net or random forest, from scratch in your language of choice in order to develop a deeper understanding.
Thank you for being kind and replying with the suggestion, I will act on it.
Hello,
I'm about to graduate with my masters degree and am working as a Machine learning engineer in R&D at a large non tech company. I wasn't able to get any research experience during my time in school, is it possible to get research experience while working? I've seen some job posting for ML engineer positions where research is published but they all require previous research experience/PhD
Would there be a way to add a shorthand stylus or photo recognition system?
Shorthand can write at about 80 to 150 wpm and is based on a phonetic style of terms.
It is used in India and by many reporters.
While niche, with the prevalence of Ipads and Microsoft Surface and Samsung Note, it seems like a way to add up to 200wpm input.
I'm looking to finetune latent diffusion models (unconditional). Does anyone have input?
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Are there any Kaggle-like competitions for reinforcement learning?
Last year in my college machine learning class, we had extra credit projects to build reinforcement learning models that were graded on their ability to play Flappy Bird. It was super fun! I just graduated this year and I would like to do more challenges in reinforcement learning, but Kaggle only seems to have challenges for supervised/unsupervised learning.
I am looking for something like Kaggle, but instead of competing on predictive accuracy in classification, you would be competing on net return in a simulated environment.
If you have worked on reinforcement learning challenges, was your experience positive or negative?
There's a bunch.
Neural MMO - runs every few months on different conferences
https://www.aicrowd.com/challenges/neurips-2022-minerl-basalt-competition
I remember there being a way for machine learning to be given unlabelled data, and then the model says which data should be labelled first. I think there was recent research on this. Does anybody know what it’s called?
Active Learning
Does anybody knows where can I learn how to create K means clustering algorithms using function for each step and only using numpy library. I don't wanna implement througb class. Thanks
Why do we pronounce "ICLR" as "eye-clear" but not "ICML" as "eye-camel?"
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Is a MacBook Pro M1 16GB RAM good enough for image classification tasks (dataset of 10,000+ images), when using Google Cloud for all the training and pre-processing or do I still need something better?
Hello, I am new to ML and have been recently practicing with Scikit-learn mainly. I have a case where i have a list of independent variables and a profit dependent variable. My question is what is the approach to know how a model can help me define which independent variables i can change to reflect a certain increase on the profit variable given the history of data? Some directions on that would be very helpful.
You may want to take a look at the Random Forest algorithm, for instance one of the introductory lectures by Nando de Freitas on YouTube on this topic. The key word is entropy, and the idea is to study how this changes when you look at all sample points with some variable value below and above some threshold value, respectively. You do this for all the variables and for each variable you also test different threshold values.
Thank you so much u/YamEnvironmental4720 for your reply. Would I be having the same results if I used the trained model's feature importance ?
It depends on how you define importance. Entropy could be one such definition but even in forest classifiers there are alternatives to entropy.
Thank you so much. My case the alternatives are on regression
Ok, in that case there is the cost function, defined on the model's parameters, that measures the average distance from the sample points to your hypothesis. This is the average error the model has for the fixed parameters. In the case of linear regression, the importance of a certain variable is given by the weight parameter attached to that variable.
If you are familiar with multidimensional calculus, the dependence of a fixed such parameter is given by the partial derivative of the cost function in this direction.
This is quite well explained in Andrew Ng's video lecture on linear regression: https://www.youtube.com/watch?v=pkJjoro-b5c&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN&index=19.
I've been interested in using ML hand writing recognition to create a font out of my own hand writing. But I don't know where to even start. Would someone know where I could find information to help me figure this out? Or if it's been done before?
To create a font you don't have to use ML. Only some photoshop skill. Do you mean to make a program that will recognize your handwriting and output the text?
Basically I have a cnc machine I want to use to write out typed documents in my handwriting. But I am not familiar with a way to build my hand writing as a font. But I stumbled on a video that was doing it in reverse where it took a page of hand written notes and typed it out. So I was trying to reverse that process
I am just learning so I won't be of help but I want to gather the requirements:
You want to create a font out of your handwriting. Does your handwriting connect the letters? If not, then are you fine with the letters like "A" being the same or have a finite amount of variations in the whole text you are making with your CNC machine? If yes then it would be easier to do it manually. Unless you want to have more than like 8 fonts I guess.
No it doesn't, and I would like some variation if possible for each letter. But I don't know if that is an option? It sound like manual is the way to go. I will look into making my own font then 🙂
When doing convolutions for cunny edge detection, is it a typical error to have the values sometimes go above 1 when normalized and that makes the picture "overexposed" at places? Should I scale everything according to the highest value to fix the images?
I have fixed it. I didn't create the magnitude at all.
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Simple question: in chain of thought reasoning, does the LLM autogenerate it's own prompt for the next step? Only the example chains are "hand made" correct?
I am using pointnet
I have a point cloud segmentation problem. In my training data, I have 1 class, but on average only ~4% of all points per point cloud are of that class, and are usually found grouped together (same object).
How do I balance this?
If I remove most points that aren't in the class, then the point cloud will become sparse and it would be too easy to spot where the class is, since only ~8% of points will remain.
Or is there a way to train this well without balancing the training data?
Thanks!
Hello everyone. I am trying to use Mallet for some basic natural language processing, but every time I attempt to execute the command, I receive this:
>Error: Could not find or load main class cc.mallet.classify.tui.Text2Vectors
>
>Caused by: java.lang.ClassNotFoundException: cc.mallet.classify.tui.Text2Vectors
Can someone help me troubleshoot?
Hello! I’m currently in school my first year for CS major, my goal is to work towards ai, deep learning to be precise… I want to find a mentor and not sure how to go about it, any ideas ? Or would it be best to gain a little more knowledge and learn python first because it’s used widely for ai (from what I’ve read) ? In college the programming language that is taught is c++. Also, How good do you have to be at math? From what I’ve researched… it is heavily math based, calculus, linear algebra, probability and statistics. I was thinking about hiring a math tutor to keep me on track but it’s insanely expensive. I did find a tutoring company that would personalize lesson plans to keep me on track but again, it’s very expensive. I do like the idea of that and think it may be worth the money because I’ll be learning from someone in the field oppose to college professors that are all over the place. Any thoughts or recommendations? I should add that I’m 37 and currently working in the medical field full time, I’m an RVT and my job is mentally and physically taxing. I’m a little nervous about a career change so late in the game but I am willing to put in the work. I think with a mentor/tutor it would take out all the hours of self research that I don’t have.
Do you have a discord? If you pm me I’ll add you on discord and I can at least get you started with python and deep learning
I do! I’ll message you 🥲
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Pitman and other shorthand handwriting systems are used in India, Nigeria and by Journalists, some medical and Administrators.
It is fairly rare but its possible to hand write at around 70 to 200wpm which is as fast as any normal person speaks.
How hard would it be to teach a visual processor to read in the text?
It does skip vowels, it can have some words identified by context (PL could be App, Apple or Apply) and text can generally be somewhat messy, but as a problem set it seems not much more difficult than recognising English handwriting or Arabic.
I am comparing models on a time series: Exponential Smoothing (Simple, Additive and Multiplicative Trend, Additive and Multiplicative Seasonal, and other combinations), ARIMA (with Python Auto_ARIMA), and a Simple Moving Average.
My concern is auto_arima optimizes parameters by minimizing the AIC (can be switched to BIC, hqic, oob). Exponential Smoothing minimizes the SSE (Sum of Squared Errors).
With them minimizing different measures, what should I use to select the lowest forecast error in model selection?
I was initially choosing the best model based on MAPE, but then I took a look at several simple exponential smoothing outputs between the optimized value for alpha/smoothing_level, and other manually inputted values like 0.4, 0.6, 0.8. What I found was the 'Optimized value' based on minimzing SSE had higher MAPE than the model instances that used alpha values I had specifically defined, whereas other error measures like the MAE, MSE, RMSE of the optimized alpha were lower. Thus, seeing that the optimized alpha produced lower of the other error measures but higher MAPE, it made me want to look for other measures.
I tried the MASE (Mean Absolute Scaled Error) (Hyndman, 2006) which was described to be appropriate against the limitations of scale-dependent errors like MAE, and percentage errors like MAPE, mainly on time series with intermittent demand or having 0 values. But I was confused because what was initially the 'best model' from my runs where I select based on lowest MAPE, would come to have a MASE > 0.90. According to Hyndman from the same article, below 1 would mean that it is better than the Naive one-step forecasts, and higher than that would worse than the naive forecasts. But also, one-step forecasts would usually have MASE < 1.0, and "Multistep MASE values are often larger than one, as it becomes more
difficult to forecast as the horizon increases." I am performing multi-step forecasts, so do I assume that 0.90 is an alright error on the best model?
I may also consider the MAE, since I am only forecasting one series at a time, and not comparing across multiple series so it does not fall under the limitation of scale-dependent errors mentioned in (Hyndman, 2006).
Hi guys I would love to start a project with AI. Right now im working on a project at my internship (as a chemical engineer) to recognise materials on a conveyor belt but it is with an already made software. I just need to add pictures and some formulas and that's it train the model with deep learning. But it made me really fascinating about starting with AI. I am good with maths and can code a bit (and got the motivation and time to learn) so I want to begin a small time project with AI to predict my car tires or oil maintenance. How would you tackle this and can someone please push me to the right direction. I think starting this with tensor flow would be good.
There’s a tensorflow tutorial for classifying mnist handwritten digits with a vanilla neural net. If you get that working and understand what it’s doing, move on to the tutorial on convolutional neural nets
Hi guys, how do you approach the interpretability of black-box models?
Thanks for the links!
Hows it going, I am currently trying to train some speech recognition models and doing some research on novel encoder architectures for e2e ASR. However I don't have a ton of compute resources. My final model will be around 300M parameters but I was wondering if training a couple of architectures at say 25-50M params and then scaling the best one is a valid approach to this problem. Why or why not?
Hello! I am currently in a small ML competition that my college lab is doing for fun. The challenge is to predict product prices. One of the columns in the dataset is the product's description and there's another one with the name.
In my head, products that include words like "phone" in the name or the description will tend be more expensive than say, a product called or described as "pencil". How should I featurize those columns to follow that logic?
if you dont have a column like "product family" try to extract this information from the name/description, for example transform anything like phone, smartphone, iphone ...etc to "phone" and drop the name/description
Hello! I have built a regression model with a total number being the dependent variable. While building I found that the predict numbers on test dataset are not even close to the actual test values which are being presented as float values as such array([4.20544375e+03, 4.02993850e+05, 2.04953309e+06, 1.06663500e+04,
4.04249688e+04, 5.66517500e+04, 3.25695500e+04, 1.62638000e+04,
5.88910625e+03, 3.54556875e+03,..............
While debugging I found that even the describe function is presenting these values as float as well:
count 1.535000e+03
mean 4.615274e+05
std 9.623142e+05
min 0.000000e+00
25% 4.907000e+03
50% 3.677500e+04
75% 3.865015e+05
max 7.319610e+06
Name: TotalValue, dtype: float64
Can anyone guide me on what is going on and how I can fix it. Please consider that I am new to machine learning. Thank you.
Finding Problematic Measurements Using Machine Learning techniques
Hello, I have a large dataset of sensor measurements (time series) that I would like to classify in a way to be able to isolate measurements that I can deem "problematic" (for example, missing samples, excessive excursions or high values, sensor malfunctioning during the measurement and so on). The metadata associated with such measurements also contains median, estimate signal to noise ratio and other metrics that I am already able to use to isolate some samples, even though always using a rule of thumb or by manually changing the thresholds for these values, which also sometimes overlap.
I'm wondering if maybe applying a clustering algorithm or other ML methods could provide me with a more general way to isolate these signals, and if so if someone knows of existing projects or papers that have dealt with this kind of classification.
Recommend a Website Links Database
I am building a classifier model that labels website links. Does anyone know of a good open-source database of website links I can use? The links need to be to English websites and have some description attached to them.
Hi All, I want to find a relatively simple ML framework best suited for the following task. Let's say I have a total of exactly 20 strings of four characters each: drta, nowm, cite, krom, etc. These strings may be combined in ways that are "correct" and in ways that are incorrect, and every combination (or "ordering") is either correct or incorrect.
My training data would consist of one thousand correct combinations one thousand incorrect combinations, something like this:
drta, cite, krom , krom, nyan; correct drta, cite, pace; correct cite, cite, pace; correct cite, cite, krom; incorrect drta, krom, cite, nyan; incorrect nyan; correct nyan, cite; incorrect cite; incorrect
And so on...
(There may be between 1 and 10 strings in each ordering.)
After training the data, I'd like to be able to input new combinations of the strings and get an AI prediction as to the likelihood that that ordering is correct (0 being definitely incorrect and 1 being definitely correct).
What do y'all think would be a good place to start? I know JavaScript and could learn some Python if necessary. I'm trying to keep it as simple as possible for now, just to get a basic model working.
Thanks for any tips!
I’m not really sure if this tasks is suited for machine learning. Using permutations and querying a dictionary will be sufficient to complete this task.
However, if you want to play around with an ML model, I suggest tokenization of the inputs and making a simple logistic regression model.
How can I joblib.dump() a model, that contains a def function in vectorizer parameter?
Hi everyone! Looking to partner with other researchers in publishing new papers in the field of NLP, recommender systems and applied ML. I’m a practitioner with over a decade of experience and lots of experience in implementing ML models in tensorflow. Any interested parties please let me know!
Hey, I am Maria, a 3rd year CS from Barcelona.
This year I decided to take ML as one of my subjects because I have always found it really interesting and so far I'm enjoying a lot this subject.
The thing is that next week I am having my midterms 😖 and the professor told us that to assess us he will give us a random problem (either regression, binary classification or multiclass classification) and a dataset. And we will have to clean the dataset, build a ML model using the dataset and evaluate it.
Even though I understand what we are doing in class I am a little bit concerned since we will only have 1 hour to build the model, clean the data, etc. So is there any strategy you guys recommend me in this case? So far we have only seen four different algorithms: linear regression, logistic regression, SVM and decision tree/random forest. What should I do when the dataset is given, which algorithms should I focus on if it has to be done in only 1 hour?
Thanks a lot! 🥰
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I’m curious if anyone has advice on setting up a computing cluster at home. I have several computers lying around and 4 CUDA enabled GPUs, and am mostly hoping to learn how distributed computing and GPUs are used for machine learning (maybe some personal or work projects on the side, as well).
So… assuming I already have the hardware in place, where should I look to get something up an running?
I am working on a project, a component of which involves taking a two images of either handwritten or digital text (mostly one word) as inputs and scoring if the two images contain the same text using only the image or shape of the text, NOT OCR.
What suggestions could you give for this? I am thinking of a transformer based Siamese network maybe?
This question that has probably crossed everyone’s mind haha, can AI win against sports betting ?
Are bookmakers already ahead of the game concerning that ?
Has anyone ever tried to develop something similar ?
Hard to say for something like this because there are so many pieces of information that are factored in that might not be able to be known until after the event begins. But I bet it would be able to crush the house at bets placed at halftime lol
I want to make youtube videos, but speaking isn't my best skill, I tend to stutter a lot, change words out of order, mispronounce things, etc.
An idea I had was to write the script and have an AI trained with my voice to read it for me, and tweak the intonation of certain phrases/words when necessary, but I don't know where exactly to start.
I have quite good knowledge of how an AI works and what limitations it has, so I believe that "training a model to a person's voice and slightly tweaking some parts of the final result" is within a regular AI's possibilities.
(yes, I know, to provide data for the AI I need to transcribe the input text, but I can take care of that)
Run a google search for like “ai generated voice copy” or the like. There aren’t a ton of up to date models for this, just because I guess they dont really find it to be super important
patpatpatpatpat t1_itttamd wrote
Newbie here who just started with Azure ML, and I have a couple of questions that I hope the experienced folks here can shed some light on.