Submitted by AutoModerator t3_110j0cp in MachineLearning
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!
Thread will stay alive until next one so keep posting after the date in the title.
Thanks to everyone for answering questions in the previous thread!
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That just explains the api usage tho, not the actual reasoning and how they do the math. Like on section 5, they create a model but don’t actually explain the parameters they use. Like why do they use a dense layer instead of something else, why do they use the adam optimizer, etc.
I don’t understand what all the terms mean, dense, lstm, optimizers, and stuff, I want to find out what these all mean and when to use then
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Welcome to the world of research. You can find all that stuff in so called "papers", i.e. publications. To get started I would suggest to have a look at one of the most influential architectures: resnet. Just Google "resnet paper" and your good to go (too lazy to fetch the citation, but it's by he et al.)
Is their a subreddit for finding specific machine learning projects?
I’d like to find something that can read text in my voice. I make a lot of recordings and it would save me tons of time if I could just have it done.
Great idea! Must be easy with some pytorch
iirc Google cloud actually offers this? https://cloud.google.com/text-to-speech/custom-voice/docs#:~:text=The%20Cloud%20Text%2Dto%2DSpeech,Text%2Dto%2DSpeech%20API.
That’s really interesting. I was hoping for something self hosted, but this may give better quality which is important. I’ll check this out sometime this week. Thanks!
I recently tried out bloom, https://bigscience.huggingface.co/blog/bloom which is supposedly the biggest open source LLM. Is this really the state of the art for language models that are publcally available?
I've been learning more about multilingual neural machine translation models lately such as the one in Google's recent paper:
Bapna, A., Caswell, I., Kreutzer, J., Firat, O., van Esch, D., Siddhant, A., Niu, M., Baljekar, P., Garcia, X., Macherey, W., Breiner, T., Axelrod, V., Riesa, J., Cao, Y., Chen, M. X., Macherey, K., Krikun, M., Wang, P., Gutkin, A., … Hughes, M. (2022). BUILDING MACHINE TRANSLATION SYSTEMS FOR THE NEXT THOUSAND LANGUAGES
I'm not sure I understand why it works for languages with no parallel data with any language though.... for instance Latinized Hindi doesn't have any parallel data. Why would the encoder or decoder representations of Latinized Hindi be compatible with any other language?
Is it because byte-pair encoding is done across languages, and that Latinized Hindi will have some word overlap with languages that DO have parallel data? So then it's encouraging the learning algorithm to represent those languages in the same latent space?
Does anyone know any study groups or any resources that targets towards learning causal inference in machine learning. I have recently started learning causal inference. Please ping me if any one interested to form a study group or something to learn.
Hi, I found this reading group on GitHub (https://github.com/fulifeng/Causal_Reading_Group) that has loads of interesting papers in the area. I would also recommend the book 'The book of why' by Judea Pearl :)
Is there an object detection algorithm that seeks to detect that there is an object as opposed to detect a specific object (cat, bike, etc.)?
I have tried searching this but generic object detection appears to be the name of the other thing...
As an example, I mean if you fed an image into this algrothm it would put bounding boxes on things without trying to identify the things.
Maybe a dumb question....
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Thanks!
Yes this is actually commonly done and a. Core ingredient in object detection. Look up faster r-cnn
Let's say I have a dataset which contains lots of inputfilename and outputfilename. How should I go ahead if I want to make a machine learning model for this, so that I later on can inject a inputfulename and get a suggestion for outputfilename?
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I'm a first year student going into computer science majoring in AI, I'm just wondering do machine learning need to know about the techniques that you learn from leetcode or not?
Is machine learning mostly about the architecture of the neural network, how to arrange it to optimise the outcome.
In the future if I wanted to be a machine learning engineer to work at for example openai (ai company) would the interview include leetcode/hackerrank questions?
Yes, you can expect leetcode-esque questions in an MLE job interview, in addition to questions about ML foundations and possibly system design questions that are geared more toward the MLE role.
I am currently a student learning ML. I have done some courses on Machine Learning and I know the theoretical part of it i.e. about algorithms like Random Forest , Decision Tree , SVM , KNN etc. But I want to work of some ML projects how should I start ? How can I gain practical knowledge of ML ? How can I make projects to improve my resume ? I particularly struggle to identify which ML algorithm is suitable for a particular problem statment?
Does anybody know whether audio visualiser model exists? I mean dynamic visualisation (kinda old Winamp visualisation that react to audio and changes constantly)
How does the facial search engine pimeyes work?
It's frighteningly accurate. I've tested on pictures of people I know and it's accurately found other pictures of them online which leads to their identity. The false positive rate is pretty low.
I have a technical background (although not CS), so please provide more than a simple response if you can.
Thanks.
Any good sources where to learn Machine Learning for free?
Honestly, YouTube is a good resource when combined with reading academic papers
100% agree. Even some tutorials from frameworks like Tensorflow
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Intro to statistical learning 2nd edition is free and amazing resource.
I have a matrix of data I want to run NMF on. The range of values is from -13.1 to 13.4. What's the best way to prep this data for NMF? I've seen people just take all the negative values and make them 0 but that seems to me like it massively cripples the variance in the data. Would it make sense to just add the absolute minimum to each value in the matrix so that it ranges from 0 to 26 instead? Or rescale the data from 0 to 1?
No idea what nmf is, but normalization is usually a critical step for any ML algo. Min max normalization is common, as well as z normalization. If your data needs to be positive, adding the minimum is indeed a way to guarantee this.
Hi all, I am doing a machine learning course on Coursera and I am using AutoML to train my dataset. While doing so, I keep getting the same error message:
The replica workerpool0-0 exited with a non-zero status of 13. To find out more about why your job exited please check the logs:
- I have tried looking online and i can't seem to find anything about error code "13"
- I have also tried to start from scratch and I keep ending up on the same issue
- I have made sure I am giving all the correct permissions
- ChatGPT-ed as well, and it further confirmed it's an accessibility issue
Have you tried doing what the message suggested, i.e. checking the logs? Otherwise I would suggest contacting their support directly.
Hello, yeah I started at it super long and hard and went through. I just ended up using the excel way instead 😂
Hello,
Most of the time I only have my old laptop available without a dGPU and a 5 year old I7 dual core.
Training on the thing takes lots of time. What could you suggest for training models online? My datasets are often in the 2-10gb Range. I don’t have a problem to pay like 30-50 Euros monthly.
I heard colab pro was super good but since they changed to the compute units model it got pretty meh? Or is it still good? Otherwise I heard about paperclip.
What else can you recommend? I only want to train models online and then export them using joblib. I am also a Student just in Case there are some nice discounts.
Appreciate any help!
Google colab gives you fast stuff for free. I trained yolo in a few minutes
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I have a question related to the Actor Critic method described in the keras example here: https://keras.io/examples/rl/actor_critic_cartpole/
I looked at the code for the Train part, and I think I understand what all lines are supposed to do and why they are there. However, I don't think I understand what role the critic plays in the improvement of the agent. To me this critic is just a value that predicts the future reward, but I don't see this being fed back into the system for the agent to select a better action to improve its reward.
Do I have a good understanding? Is the critic just a "bonus" output? Are the two unrelated and the exact same performance could be achieved by removing the Critic output altogether? Or is the critic output used in any way to improve learning rate in a way I fail to see?
Thank you.
so actor critic without critic is just policy gradient/reinforce/score function gradient, first two names used in RL, last one used in stats/OR.
short answer is policy gradient tends to have high variances empirically, so people use control variates to control its variance, and the critic is simply the control variate.
high variance methods usually converge to worse local minimas than low variance ones. u can verify this by taking or the critic function entirely. try it itself with that tutorial
Hello! I`m currently a 2nd year data science student and I am into machine learning engineering as a career, and I`m wondering what skills should I learn on my own beside (python ml frameworks) and data engineering frameworks such as pyspark, I was considering to learn java but I am not sure if I am better off investing my time learning something else
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For the Huggingface Inference API,
How can I request for a custom image size? I am able to do this in a gradio space, but when using the API from python, I can't seem to find some sort of input parameter for output size...
How do LSTMs for sports prediction work? My understanding with LSTMs is that they're predicting the next step in a sequence, but a sports match is two sequences coming against each other.
Is it possible to run DeepSpeed+ZeRO with a Tesla P40 (24Gb) to make use of my 256gb main system memory during training? If so are there any examples of this particular setup or required cuda driver versions?
Is it possible on Azure Machine Learning to run a notebook as an API? If yes where can I find this API ?
A4000 or 4070TI for transformers in NLP (Bert, LayoutLM, ...)?
I am currently a graduate student in Computer Science and am taking a class that talks about the foundation of Machine Learning. The class is very math rigorous in nature.
The textbook that we use is Foundations of Machine Learning by M. Mohri, A. Rostamizadeh, A. Talwalkar.
I am seeking a paid private tutor to help me with the content and homework of the class. Pay is negotiable!
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Question on transformer architecture:
If the task is simply to generate data given a context of data generated so far (such as in the case GPT-3), then can the architecture be simplified?
(The separation of the encoder and decoder layers seems arbitrary when they are processing the exact same data)
Is the next step in LLMs to predict the entire next sentence?
From what I understand LLMs mostly just predict the next word in a sentence. With just this we have seen HUGE advancement and emergent behavior out of what could essentially be called level 1 of this tech. So then would making a machine learning architecture to predict the entire next sentence be the next logical step? After that would it be entire paragraphs? What would be the challenges of making such an architecture?
It doesn't look like it's headed that way, no. The set of possible next sentences is just too big to iterate over or to compute a softmax over, so it's broken down into words. In fact, the set of possible words is often too big so it's broken down into subwords with methods like byte pair encoding and WordPiece.
The key when dealing with predicting one word or subword at a time is to model long-range dependencies well enough so that the LM can generate coherent sentences and paragraphs.
Makes sense. To expand on the number of possible iterations wouldn't it be something akin to a collapsing wave function? Like trying to iterate through all possible responses would be impossible, but the list of probable responses shrinks as the context expands.
For example if I just input "knock" there are too many possible sentences to search, but if I input "knock knock". The most likely response is "who's there?" A simple example sure, but you get the point yea?
In terms of probabilities yeah that's right.
In the actual code, it's most common to do a softmax over the output vocabulary. In practice that means the model computes the probability of every possible next output (whether word or subword) and then we sort it, take the argmax, or the top K depending on the problem.
I think about generating one word at a time as a key part of the way we're searching through the space of probable sentences, because we can't afford to brute-force search.
Got it. Thanks for the information.
I am not a scientist or PhD holder but really fascinated by what ML can do and thus leveling up through a bootcamp to learn DS and ML. My question is how to get into ML research while doing my day job? I am interested in how ML can be used for CV as well as areas of cybersecurity. How should a person like me go about researching a simple topic and collaborate with more experienced community? TY for any guidance.
ML is a mathematical discipline. You have to read books to dive into it. Collaboration is possible after you become usefull. Try "Grocking deep learning" for simple introduction to neural networks. Also check classical ml tasks in regression/classification/trees and drill them. This is hard work wich can not be substituted by being part of some community.
Update: Before it you better learn basics of python programming language. Find lectures with homeworks which are not connected with ML itself (16 hours + 40 hours will be enough)
Thanks for details.
My journey started years ago by wanting to understand the DQN paper. Hintons coersera course was a nice start and after that it was just going down the rabbit hole which are citations. It takes a lot of effort in the beginning because every single sentence you read will introduce new topics to you that you never heard before. But after a while these become second nature and you won't spend any second thoughts on them anymore. It just takes preserverance and will imo.
Thanks for sharing.
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I want to thank community for this possibility to ask simple time series question. Please don't reply "jump in window" (it is bad advice from statistical PoV since I'm on the second floor)
I'm new to time series topic in particular and in ML in general. I have tried ARDL model with no seasonal part and no exog variables (from statsmodels.tsa.api import ARDL). I'm working with very small dataset of 16 points (see Appendix 1) with strict trend component.
This TS is stationary according to adfuller test inspite of it is not stationary due to simple criteria like "moving average have to be kind of constant". Not sure if this test even applicable for such a small number of points.
Imagine i want to forecast next nine(sic!) points and i have no idea how to choose best number of lags. Hence I fit my model for several different nlags on TS[:-9] dataset (train set) and choose the best lag by comparing MAE/MSE/R2 on TS[-9:] dataset (test set). Best lag is lags = 1.
In spite of all ugliness of the idea to forecast 9 points having 16-9=7 points the prediction plot is well fitted with test data plot. This result convinced me to to go further (from common mathematical sense).
Now I have to decide :
(1) to use the model above (trained on TS[:-9] set) to predict TS[16:26] values for which i have very good R2 on nine predictions.
(2) or i have to refit the lags = 1 model with all my points ( TS[:] ) but without the chance to test it for nine predictions
And i have no idea how to choose the best option, so i decided to research convergence of model's coefficients (m.params). My plan is to fit nine models for nine sets TS[:-9], TS[:-8], TS[:-7],...TS[:-0] and to check whether a and b in nine consecutive models y(t) = a*y(t-1) + b are tending to converge to two a_lim, b_lim constants. They are not. Not even close to convergence. They look random... This is the end, i don't know how to choose.
My very last idea was to freeze b = constant for all nine models and retest the convergence of a under this restriction but i see no such option in ARDL (and to be honest i have no idea how to program ARDL-like function by myself even for lag=1)
My question is: Any ideas what i can and should do?
Btw, in appendix 2 I have tried to research coefficient's convergence for function:
f[i] = 1.01*f[i-1]+0.01+random noise
I see some problems with convergence even in this scenario.
Appendix 1: Demographic data (fact)
year
2006-01-01 87287
2007-01-01 86649
2008-01-01 86036
2009-01-01 85394
2010-01-01 84845
2011-01-01 84542
2012-01-01 84034
2013-01-01 83881
2014-01-01 83414
2015-01-01 83035
2016-01-01 82656
2017-01-01 82280
2018-01-01 81654
2019-01-01 81745
2020-01-01 81614
2021-01-01 81367
Name: num_of_citizens, dtype: int64
​
Appendix 2: convergence in model task
import pandas as pd
# genrate data
f = [1,1]
for i in range(2,2000):
f.append((1.01*f[i-1]+0.01))
print(len(f))
df = pd.DataFrame({'fib_num':f})
df.head(10)
#df.plot(subplots=True, layout=(1,1), legend = True, figsize = (7,7))
import numpy as np
std = (max(f) - min(f))*0.0001
f_noise = [x + np.random.normal(loc = 0, scale = std) for x in f]
print(f'Max = {max(f_noise)}, Min = {min(f_noise)}')
df_noise = pd.DataFrame({'fib_num_noise':f_noise})
#df_noise.plot(subplots=True, layout=(1,1), legend = True, figsize = (5,5))
df = df_noise.rename(columns={'fib_num_noise':'fib_num'})
from statsmodels.tsa.api import ARDL
fib_par = {}
r2s = []
mae = []
rmse = []
for k in range(15, df.shape[0]):
partial_set = np.asarray(df['fib_num'][0:k])
m = ARDL(partial_set, lags=1)
mfitted = m.fit()
partial_set_pred = (mfitted.predict(start = 0, end = k-1))[2:]
r2s.append(r2_score(partial_set[2:],partial_set_pred))
mae.append(mean_absolute_error(partial_set[2:],partial_set_pred))
rmse.append(np.sqrt(mean_squared_error(partial_set[2:],partial_set_pred)))
fib_par[k] = mfitted.params
# print one of the last coeff-s in coef dict:
print(fib_par[df.shape[0]-20])
# this is plot for 'a' (Y = a*Y +b) change to !=1 to see plot for 'b'
for v in range(len(fib_par[15])):
if v != 0:
pd.Series([x[v] for x in fib_par.values()]).rename(v, inplace = True).plot(legend = True, figsize = (25,7), title = 'Model coeffs')
edf = pd.DataFrame({'r2score':r2s, 'mae':mae, 'rmse':rmse}).iloc[:200]
edf.plot(legend = True, figsize = (15,7), subplots=True, layout=(3,1), title = 'Model quality params')
I'm having trouble understanding a lot of explanations of different neural networks online because I can't wrap my head around any of the diagrams. Any good resources that do a good job linking the diagrams to the mathematical equations because I am completely lost
I haven't seen a guide on that, but I remember it being challenging! Feel free to post one that's giving you trouble.
If i were to start learning ML today suggest some online resources that guide you along the way on a project by project basis
What matters while running models?
hey guys, I'm new to machine learning and just learning from the basics. I am planning to buy a GPU soon for running pre-built models from google colab.
My question is after you build a model what matters for the models runtime? Is it the Memory, the bandwidth or the cuda core you utilize?
Basically what makes an already trained model run faster when using in application? I can imagine it may vary from application to application, but just wanted to learn what matters the most when running pre trained models?
I would more memory is more important. Buy the 3060 with the 12GB. If you have more money get the 3090 24GB. The memory is more important in my view because it will allow you to run bigger models.
How is the cost of queries to AI tools such as ChatGPT determined?
Sorry for the beginner question, but I keep seeing numbers such as 2 cents per query being quoted for a ChatGPT query.
How much processing power is required to complete a query? Does it scale with the number of parameters - or does number of parameters only affect memory usage?
They charge per 1000 tokens which is about 750 words. The rate for 1000 tokens is a few cents.
Say there’s a model with double the parameter, will it take twice as long to process?
I’m not sure what the time complexity is
What are some good tools for data pipelines that scale well? I'm locked into Jax/Flax for work, but would like to disconnect from TensorFlow to the greatest extent possible. I was looking at the huggingface dataloaders, does anyone have experience with those?
I have years of grievances with io. It's really difficult to have something that is both flexible, performant, and can scale to terabytes of data with complex strucuture. As soon as you leave the nice cv or nlp domain you are on your own. Raw c type arrays loaded manually from disk in a separate Cuda stream can sometimes be really be your best shot.
yeah, this has been my experience -- i'm working with a lot of custom data, and even though some of it is CV adjacent, it doesn't fit exactly (e.g., ~40 channels instead of 3 like RGB). would be nice, especially for research prposes, to have something to plug and play that just worked.
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I am trying to find tasks that use discrete tokens as inputs to do classification. E.g. some NLP classification tasks operate on a set of tokens (words, characters, special token sets, etc), and malware classification can operate on raw bytes. Is there any other domain that uses discrete sequences of tokens as inputs?
Genomics data can be
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I noticed ChatGPT can show me some example code but it's far from working (eg. Variables not defined...).
My project: I try to track fishes in a pond filmed from above and calculate their speed to see health issues if their movementspeed changes. For training I have videos of different daytimes.
ChatGPT gave me code but never told me really how to train a model on the PC that will work good enough on a Raspberry Pi 3 or 4. Is there any "known to work" code or tutorial that I can use to start my project from? I did some stuff in Python on the Pi, but I'm far from a programmer. Help?
Leave it be? Or put in the effort and learn programming and numeric methods in general, e.g. HMMs. "I'm far from a professional mountain biker but I want to race down this difficult trail"
Great help 🤦♂️
You could look at a YOLO tutorial, that's how I started back whenn YOLO was still new .
Maybe this repo will help you:
https://github.com/ultralytics/yolov5
But keep in mind that you will have to learn AI techniques to really be able to control the outcome of your model and that the training process can become unnecessarily expensive if you don't know when and how to optimize it.
Any MS level statisticians who have moved into ML research?
Hi guys,
I’m looking into machine learning and it’s use in shark conservation. The below figure shows the effectiveness of image classification of sharks.
Can anybody help me interpret this? The internet is telling me that if you follow two species to where they meet, the colour in the square represents how often one has been mistaken for the other. But if this is the case, why is there a uniform line down the middle showing a much higher number?
Thanks in advance from a confused biologist…
The “line down the middle” is where the “predicted” shark is the true shark (the type of shark was guessed correctly. The color of the square represents the certainty of the guess. *How confident is the software that it made the “right” guess? In these cases, it’s 100% confident since it is the right one!)
Pretty cool if you ask me!
You can match them up to see for yourself.
Is it psychotic to use a GPT based model for what could be treated as image segmentation?
For my task, I trained a GPT model to predict a mask for an Input Integer Matrix with certain rows being entirely a spurious value. Where the mask is replacing the spurious integers with X's. It is a text based model for what could be considered an image task.
It is not uncommon anymore to model images as patches of tokens, and then send in the sequence to a transformer-based model. So not psychotic at all.
See An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.
What's it like being a Machine Learning engineer, really? I understand you're doing a lot of 'data pipelining', but how much building are you actually doing? Ever since I've started programming I've thought of over 25 projects to build... but only a subset feature ML concepts. I'd want to be able to program/build extensively but not exclusively using ML - am I better off being a SWE? PS: Currently in an MSCS, very ML-heavy.
I'd also appreciate resources/links to help guide me/understand the role a bit better.
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Hey,
Many of the proposed legal regulations for systems such as autonomous vehicles mention the need for explainability or transparency in the decision making processes of said vehicles. My understanding however was that due to their deep-learning processes, this is either extremely hard or impossible to do?
Is my understanding correct? Or is explainability possible in deep-learning systems?
What’s the current best AI to clone voices that I can run on my local pc?
I don't know if it is the current best AI to clone voices, but there is a zero-shot model named YOUR-TTS, it has pre-trained weights available and you only need around 1 min of your voice to make it sound quite similar. But you can always retrain it with more samples of your voice to improve the performance even more.
https://github.com/Edresson/YourTTS
I think it was also added to the Coqui-TTS toolkit.
https://github.com/coqui-ai/TTS
However I only played around with the demos in the original repo, and therefore don't know how to use it if you are serious about voice cloning.
Looking for recommendations on PhD-level papers/textbooks/reading list on Machine Learning.
I want to revisit even the most "basic" of topics such as linear/logistic regression, but with better deeper understanding.
Desired outcome: able to answer questions like
TIA!
Pattern Recognition and Machine Learning (PRML) and Elements of Statistical Learning (ESL) are two of the standard references that will give you what you're looking for with regards to the more classical topics you allude to (linear models, kernels, boosting, etc.).
Briefly looked through and I think you're spot on.
Please feel free to throw more leads my way, thank you!
Are there any developments in the ASIC/analog computing space that people are really excited about? I think most people know about google's TPUs by now, but is there anything else with the potential to threaten the dominance of GPUs in the next few years?
Not a direct answer to your question, but I interviewed a while back with this company called Fathom Radiant that's developing an optical computing system for AI training. It's further off than the next few years, but their ultimate goal is to use that technology to establish a computing center that outcompetes other options for training very large models, then leverage that position to support AI safety by allocating resources to groups that meet certain safety standards. It's an interesting approach and seems like a cool technology.
What is the best way to clone a voice in real time TTS? Elevenlabs? I've been trying to use tortoise tts fast branch and tried a few others and they aren't producing the quality I want or at the speed I need. I haven't tried elevenlabs api, is it fast?
I am looking for a dataset of Compilable source codes, labeled with their vulnerability CVE.
used for training a static analysis framework, it must be compilable though, meaning the source code should be complete.
Currently we are using Juliet test suites as a dataset, but it is purely synthetic and not human-generated.
Hi all, I am looking for datasets on climate patterns in sub Saharan west Africa as well as data sets on crop production and yields for the same area. Any good sources available?
Hi everyone, I am looking for something like CLIP in speech area, which could measure the distance between text and speech (Mel-spectrum).
I found speech-CLIP before but unfortunately, its input for speech is raw wave rather than Mel-spectrum (same with HuBERT). I would be so appreciate if you can provide some information about that!
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My goal is to deploy an ML model which can perform price prediction on an exchange traded fund (ETF) - which is essentially an aggregation of stocks. A very popular ETF is the S&P 500 (which is not actually the ETF I want to predict on. The one I want to predict on only has about 30 stocks).
Can an ML model trained/tested on individual stocks which are *not* in the ETF a valid way of building a model which can accomplish price prediction on an ETF?
I work in NLP. Work mainly consists of fine tuning NLP models. With the rise of LLMs I'm seeing a lot of my work becoming Prompt engineering. I'm happy to pick up the new skill but I'd like to know what avenues I have to upskill beyond being a prompt engineer without a PhD. Feels like all the learning I did on model architectures etc is going to waste. There are still a few projects that need me to fine tune a model for text classification etc but as LLMs get better I suspect I need better skills to go beyond becoming a prompt engineer. For anyone else in NLP who doesn't have a PhD and doesn't have any experience building model architectures/training from scratch etc, how are all of you trying to up skill in these times? EDIT: Worded the question to ask only people who don't have a PhD, I would actually like to know everyone's perspective on this.
Are there any examples of learning higher-rank tensors in machine learning?
I am going to start my thesis soon.
any idea on where to start with anomaly detection? We have a huge amount of data. It's used for site reliability engineering! Any help welcome!
Convoluted neural networks work on images. What’s the equivalent for videos?
The same but feeding frames from the videos into model like a CNN-RNN model which keeps memory of previous frames, but will obviously be a much bigger dataset.
I’m trying to pd.read_csv some large data files and put them into data frames for a nER project. But the kernel keeps crashing. This is in a VSCode Jupiter notebook by the way. Please let me know what i can do instead, thanks.
Sounds like a memory problem - do you have enough RAM freed up to load these files into? That's what my problem was when I had a similar problem.
Know i have a terrible machine i guess.
Not necessarily - could just be a huge data file. Maybe pandas can read in parts of it at a time? I'm not sure, but it could be worth looking into.
I am a Software Engineering student. I want to use Yolov8 to make a barbell move track tracker as my homework. Here are more details:
Input: The user can upload a video of using barbell to exercise at the frontend (A webpage).
Handle: Get the video, handled it by yolov8, and get a new video which draw the movement track of barbell with lines.
output: The user can get the new video at the webpage.
How to use yolov8 to handle it? Where can I find the model? If I need to train a model by myself, where to find the dataset?
Thanks for you guys' help. You are all gigachad.
I need to categorize products according to their product name.
I have a huge amount of data hand categorized by humans.
I am a Python developer but my knowledge on this matter is zero.
Do you have any recommendation as to where to start? any topic, reading or resource is welcome.
Of course I've been researching myself and it seems MuiltiClass Classification is what I need. But I'd like some extra opinions and pointers.
Are there any cloud services I could leverage by paying? This is also an option.
What does it meant by this statement: “GM (Gaussian mixture) on its own is not much of use because it converges too fast to a non-optimal solution”
I dont know what GM is, but for the second part, you can imagine that training a model is like finding a set of optimal conditions. However, some models find good conditions but not the best ones, this normally depends in the Loss function and other characteristics.
Oh wow okay, that makes a lot of sense! So essentially “converging” means, in simpler terms, that the model comes to a conclusion. And what you’re saying is that the model comes to a conclusion too fast on its conditions, and these are good conditions but not the optimal ones.
Yes, the model "thinks" the solution found is the best, but it is not. The model is getting confused because of some complex mathematical results that it gets along the way, and never gets to the optimal solution hence "non optimal solution".
Sometimes it goes even worse: not only it does not converge to the best solution (previous paragraph) but also diverges, i.e the error increases (value grows) instead of decrease. This is less common and maybe just means there are planning errors.
This is just a broad idea.
Has anyone made spectrogram 2 spectrogram models for music analysis, specifically demixing such as isolating vocals? I’m currently using a Mel spectrogram for the input and output but struggling to get good results. Using a hop length=512,n_fft=2048, no_mels=128. My model is currently a bi directional GRU model with 3 layers and a 256 hidden size.Does anyone know a good model type to use and/or good audio transformations for this project?
Cheers
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How much theoretical speedup do you think DL could get if we coded everything directly in C++ instead of Python?
M1 Macbook vs Intel I5 Macbook for ML
Good day to all users! I am considering to purchase either M1 Air Macbook or I5 quad-core Macbook Pro 2019/2020 for my upcoming AI bachelor course. I found out it would be problematic sometimes for ML to be done using M1. At the same time I won’t be able to purchase other laptops for another ~5-7 years.
Which device would you recommend if you would be forced to choose between those two?
I have just been testing out some machine learning as I am new to it and have a simple dataset currently 500k rows the target value is literally the sum of each row. I was using model = Sequential()model.add(Dense(4, input_dim=4, activation='ELU'))model.add(Dense(1, activation='linear'))# Compile the modelmodel.compile(loss='mean_squared_error', optimizer='adam') model.fit(X, y, epochs=4000, verbose=1, batch_size=120). I then fed the model some unseen data to see if it could make the new calculations again literally just sum up the values.
22000 - 11000 - 6000 - 1500 should equal 3500 but instead, i got 3499.9915. The results for the other new unseen data was all similar I was wondering how i can fix this I know ai models need a lot of data but surely for something so trivial I would have expected it to get the values perfectly correct. My long term goal is to build data validation through calculations which is why i am practicing such a basic model.
Is there an equivalent of "Thomas's calculus" for Linear Algebra?
I am doing my very first machine learning project and am hoping to follow the word intrusion and topic intrusion human judgement/validation tasks outlines by Chang, et al 2009 <- https://proceedings.neurips.cc/paper/2009/file/f92586a25bb3145facd64ab20fd
can someone please break down these equations for me/ provide hypothetical numbers to plug in? thank you so much:
TheGamingPhoenix_000 t1_j8p1o9l wrote
Dumb Question: Where is a good resource to understand the actual math going on, most resources I find with a simple google search is only api usage, not actually what all the parameters and such mean