I want to create a web application which uses machine learning to predict the price of agriculture commodities before 2-3 months.
Is it really feasible or not?
If yes, then please provide some rough idea about which tools and technologies I can use to implement it.
First of all, study math, more precisely, statistics and differential algebra.
Then, use any open (or not) source neural networking libraries you could find. Even MATLAB would help, as it has a good set of examples (I think it has some of alike prediction models, at least I remember creating a model for predicting election results in Poland)
Decide on your training and input data. Research how news and global situation influences commodity prices. Research how existing bots predict prices for next 1-2 minutes. Also consider using history of predictions from certain individuals, I think Reuters has some API for this. Saying this I imply you'll have to integrate natural language processors, too.
Train your model, test it, improve it for quite a long time.
Finally, deploy a boring front-end and monetize it.
If you dont want to implement ML, you can also use kalman filters.
Related
Currently, I'm working on a project where I need to extract the relevant aspects used in positive and negative reviews in real time.
For the notions of more negative and positive, it will be a question of contextualizing the word. Distinguish between a word that sounds positive in a negative context (consider irony).
Here is an example:
Very nice welcome!!! We ate very well with traditional dishes as at home, the quality but also the quantity are in appointment!!!*
Positive aspects: welcome, traditional dishes, quality, quantity
Can anyone suggest to me some tutorials, papers or ideas about this topic?
Thank you in advance.
This task is called Aspect Based Sentiment Analysis (ABSA). Most popular is the format and dataset specified in the 2014 Semantic Evaluation Workshop (Task 5) and its updated versions in the following years.
Overview of model efficiencies over the years:
https://paperswithcode.com/sota/aspect-based-sentiment-analysis-on-semeval
Good source for ressources and repositories on the topic (some are very advanced but there are some more starter friendly ressources in there too):
https://github.com/ZhengZixiang/ABSAPapers
Just from my general experience in this topic a very powerful starting point that doesn't require advanced knowledge in machine learning model design is to prepare a Dataset (such as the one provided for the SemEval2014 Task) that is in a Token Classification Format and use it to fine-tune a pretrained transformer model such as BERT, RoBERTa or similar. Check out any tutorial on how to do fine-tuning on a token classification model like this one in huggingface. They usually use the popular task of Named Entity Recognition (NER) as the example task but for the ABSA-Task you basically do the same thing but with other labels and a different dataset.
Obviously an even easier approach would be to take more rule-based approaches or combine a rule-based approach with a trained sentiment analysis model/negation detection etc., but I think generally with a rule-based approach you can expect a much inferior performance compared to using state-of-the-art models as transformers.
If you want to go even more advanced than just fine-tuning the pretrained transformer models then check out the second and third link I provided and look at some of the machine learning model designs specifically designed for Aspect Based Sentiment Analysis.
I want to train and use an ML based personal voice to text converter for a highly impaired voice, for a small set of 300-400 words. This is to be used for people with voice impairment. But cannot be generic because each person will have a unique voice input for words, depending on their type of impairment.
Wanted to know if there are any ML engines which allow for such a training. If not, what is the best approach to go about it.
Thanks
Most of the speech recognition engines support training (wav2letter, deepspeech, espnet, kaldi, etc), you just need to feed in the data. The only issue is that you need a lot of data to train reliably (1000 of samples for each word). You can check Google Commands dataset for example of how to train from scratch.
Since the training dataset will be pretty small for your case and will consist of just a few samples, you can probably start with existing pretrained model and finetune it on your samples to get best accuracy. You need to look on "few short learning" setups.
You can probably look on wav2vec 2.0 pretrained model, it should be effective for such learning. You can find examples and commands for fine-tuning and inference here.
You can also try fine-tuning Japser models in Google Commands for NVIDIA NEMO. It might be a little less effective but could still work and should be easier to setup.
I highely recommend watching the youtube original series "The age of AI"'s First season, episode two.
Basically, google already done this for people who can't really form normal words with impared voice. It is very interesting and speaks a little bit about how they done and doing that with ML technologies.
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I have some 360 odd features on which I am training my neural network model.
The accuracy I am getting is abysmally bad. There is one feature amongst the 360 that is more important than the others.
Right now, it does not enjoy any special status amongst the other features.
Is there a way to lay emphasis on one of the features while training the model? I believe this could improve my model's accuracy.
I am using Python 3.5 with Keras and Scikit-learn.
EDIT: I am attempting a regression problem
Any help would be appreciated
First of all, I would make sure that this feature alone has a decent prediction probability, but I am assuming that you already made sure of it.
Then, one approach that you could take, is to "embed" your 359 other features in a first layer, and only feed in your special feature once you have compressed the remaining information.
Contrary to what most tutorials make you believe, you do not have to add in all features already in the first layer, but can technically insert them at any point in time (or even multiple times).
The first layer that captures your other inputs is then some form of "PCA approximator", where you are embedding a high-dimensional feature space (359 dimensions) into something that is less dominant over your other feature (maybe 20-50 dimensions as a starting point?)
Of course there is no guarantee that this will work, but you might have a much better chance of getting attention on your special feature, although I am fairly certain that in general you should still see an increase in performance if the single feature is strongly enough correlated with your output.
The other question that is still open is the kind of task you are training for, i.e., whether you are doing some form of classification (if so, how many classes?), or regression. This might also influence architectural choices, and the amount of focus you can/should put on a single feature.
There are several feature selection and importance techniques in machine learning. Please follow this link.
A wanna-be data-scientist here and am trying to understand as a data scientist, when and why would you use a Probability Density Function (PDF)?
Sharing a scenario and a few pointers to learn about this and other such functions like CDF and PMF would be really helpful. Know of any book that talks about these functions from practice stand-point?
Why?
Probability theory is very important for modern data-science and machine-learning applications, because (in a lot of cases) it allows one to "open up a black box" and shed some light into the model's inner workings, and with luck find necessary ingredients to transform a poor model into a great model. Without it, a data scientist's work is very much restricted in what they are able to do.
A PDF is a fundamental building block of the probability theory, absolutely necessary to do any sort of probability reasoning, along with expectation, variance, prior and posterior, and so on.
Some examples here on StackOverflow, from my own experience, where a practical issue boils down to understanding data distribution:
Which loss-function is better than MSE in temperature prediction?
Binary Image Classification with CNN - best practices for choosing “negative” dataset?
How do neural networks account for outliers?
When?
The questions above provide some examples, here're a few more if you're interested, and the list is by no means complete:
What is the 'fundamental' idea of machine learning for estimating parameters?
Role of Bias in Neural Networks
How to find probability distribution and parameters for real data? (Python 3)
I personally try to find probabilistic interpretation whenever possible (choice of loss function, parameters, regularization, architecture, etc), because this way I can move from blind guessing to making reasonable decisions.
Reading
This is very opinion-based, but at least few books are really worth mentioning: The Elements of Statistical Learning, An Introduction to Statistical Learning: with Applications in R or Pattern Recognition and Machine Learning (if your primary interest is machine learning). That's just a start, there are dozens of books on more specific topics, like computer vision, natural language processing and reinforcement learning.
Objective: a node.js function that can be passed a news article (title, text, tags, etc.) and will return a category for that article ("Technology", "Fashion", "Food", etc.)
I'm not picky about exactly what categories are returned, as long as the list of possible results is finite and reasonable (10-50).
There are Web APIs that do this (eg, alchemy), but I'd prefer not to incur the extra cost (both in terms of external HTTP requests and also $$) if possible.
I've had a look at the node module "natural". I'm a bit new to NLP, but it seems like maybe I could achieve this by training a BayesClassifier on a reasonable word list. Does this seem like a good/logical approach? Can you think of anything better?
I don't know if you are still looking for an answer, but let me put my two cents for anyone who happens to come back to this question.
Having worked in NLP i would suggest you look into the following approach to solve the problem.
Don't look for a single package solution. There are great packages out there, no doubt for lots of things. But when it comes to active research areas like NLP, ML and optimization, the tools tend to be atleast 3 or 4 iterations behind whats there is academia.
Coming to the core problem. What you want to achieve is text classification.
The simplest way to achieve this would be an SVM multiclass classifier.
Simplest yes, but also with very very (see the double stress) reasonable classification accuracy, runtime performance and ease of use.
The thing which you would need to work on would be the feature set used to represent your news article/text/tag. You could use a bag of words model. add named entities as additional features. You can use article location/time as features. (though for a simple category classification this might not give you much improvement).
The bottom line is. SVM works great. they have multiple implementations. and during runtime you don't really need much ML machinery.
Feature engineering on the other hand is very task specific. But given some basic set of features and a good labelled data you can train a very decent classifier.
here are some resources for you.
http://svmlight.joachims.org/
SVM multiclass is what you would be interested in.
And here is a tutorial by SVM zen himself!
http://www.cs.cornell.edu/People/tj/publications/joachims_98a.pdf
I don't know about the stability of this but from the code its a binary classifier SVM. which means if you have a known set of tags of size N you want to classify the text into, you will have to train N binary SVM classifiers. One each for the N category tags.
Hope this helps.