I was working on a Keras LSTM for predicting lottery numbers in jupyter. I have a csv with the complete history of 6000 winning sets of numbers. I started using Colab to speed things up and upgraded to Pro+ but there was no difference with the upgraded processors. I came across FastAI and I have everything installed. I am completely new to FastAI and the tutorials seem complicated for serious projects.
Is there any FastAI tutorials to predict lottery numbers (6/39)?
If not how would I approach this?
What area should I focus on?
Thanks in advance...
FastAI is great at supporting easy out-of-the-box solutions for a few key problem types: namely natural text, image, tabular, and collaborative filtering. Your use-case is not exactly any of these, but I expect it is possible to use the text model functionality. This tutorial shows how an LSTM language model learns to predict the next word given a sentence which seems very similar to your use-case.
You will want to avoid the tokenization and numericalization steps of the text model, since you already have numerical tokens in place of words. I imagine you can get something to work either by explicitly turning those steps off or by passing in the lists as space-separated text and letting it re-tokenize/numericalize.
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In the traditional "one-hot" representation of words as vectors you have a vector of the same dimension as the cardinality of your vocabulary. To reduce dimensionality usually stopwords are removed, as well as applying stemming, lemmatizing, etc. to normalize the features you want to perform some NLP task on.
I'm having trouble understanding whether/how to preprocess text to be embedded (e.g. word2vec). My goal is to use these word embeddings as features for a NN to classify texts into topic A, not topic A, and then perform event extraction on them on documents of topic A (using a second NN).
My first instinct is to preprocess removing stopwords, lemmatizing stemming, etc. But as I learn about NN a bit more I realize that applied to natural language, the CBOW and skip-gram models would in fact require the whole set of words to be present --to be able to predict a word from context one would need to know the actual context, not a reduced form of the context after normalizing... right?). The actual sequence of POS tags seems to be key for a human-feeling prediction of words.
I've found some guidance online but I'm still curious to know what the community here thinks:
Are there any recent commonly accepted best practices regarding punctuation, stemming, lemmatizing, stopwords, numbers, lowercase etc?
If so, what are they? Is it better in general to process as little as possible, or more on the heavier side to normalize the text? Is there a trade-off?
My thoughts:
It is better to remove punctuation (but e.g. in Spanish don't remove the accents because the do convey contextual information), change written numbers to numeric, do not lowercase everything (useful for entity extraction), no stemming, no lemmatizing.
Does this sound right?
I've been working on this problem myself for some time. I totally agree with the other answers, that it really depends on your problem and you must match your input to the output that you expect.
I found that for certain tasks like sentiment analysis it's OK to remove lot's of nuances by preprocessing, but e.g. for text generation, it is quite essential to keep everything.
I'm currently working on generating Latin text and therefore I need to keep quite a lot of structure in the data.
I found a very interesting paper doing some analysis on that topic, but it covers only a small area. However, it might give you some more hints:
On the Role of Text Preprocessing in Neural Network Architectures: An Evaluation Study on Text Categorization and Sentiment Analysis
by Jose Camacho-Collados and Mohammad Taher Pilehvar
https://arxiv.org/pdf/1707.01780.pdf
Here is a quote from their conclusion:
"Our evaluation highlights the importance of being consistent in the preprocessing strategy employed across training and evaluation data. In general a simple tokenized corpus works equally or better than more complex preprocessing techniques such as lemmatization or multiword grouping, except for a dataset corresponding to a specialized domain, like health, in which sole tokenization performs poorly. Addi- tionally, word embeddings trained on multiword- grouped corpora perform surprisingly well when applied to simple tokenized datasets."
So many questions. The answer to all of them is probably "depends". It needs to be considered the classes you are trying to predict and the kind of documents you have. It's not the same to try to predict authorship (then you definitely need to keep all kinds of punctuation and case so stylometry will work) than sentiment analysis (where you can get rid of almost everything but have to pay special attention to things like negations).
I would say apply the same preprocessing to both ends. The surface forms are your link so you can't normalise in different ways. I do agree with the point Joseph Valls makes, but my impression is that most embeddings are trained in a generic rather than a specific manner. What I mean is that the Google News embeddings perform quite well on various different tasks and I don't think they had some fancy preprocessing. Getting enough data tends to be more important. All that being said -- it still depends :-)
I saw different articles speaking about OCR form recognition (data extraction) and they said that they used Neural Network in order to do form recognition, so what's the relation between Artificial Neural network (ANN) and form recognition? If I want to extract fields from a BusinessCard, is it required to use ANN or is it optional? In other words when do I need to use ANN and when I don't?
It's a little different. ANN is just an "expert" in all OCR. But OCR engines contain many experts. When you study ANN you will build a simple OCR engine using just ANN but this does not compare to modern engines that use this in conjunction with tri-grams, morphology, data types ( very important for BCR and Forms ), dictionaries, connected components algorithm, etc. So look at it as just one of the tools in the bag of tricks to extract quality results. A good engine will incorporate ANN and all the others. In BCR there are additional considerations and it should be very heavy on connected components, dictionaries first, then use ANN and pattern matching for the actually recognition.
ANN is one way to perform OCR. There are others. Hence if you want to extract fields from a BusinessCard using ANN is only optional.
Good question. I recently spent some time playing with OCRopus, a Google project that does OCR - you can get it for free and play with it yourself. I'm pretty sure that it has an ANN as one of the modules behind it. However, the whole process of Optical Character Recognition can have many steps (lots of different little modules that each do something and pass the results to the next module).
So, here are some of the things I remember as being done by modules in that project:
There was a module that turned the image into black and white - this makes it easier for later modules to deal with.
Getting rid of speckles / spackles.
Straightening out the lines of text.
Breaking lines of text into individual words (it's been a few weeks, not sure about this one)
Basically, you can do the above using little bits of code that don't involve a neural net. So it's simpler doing it with these little bits of code.
The neural net I think is used just to recognize the individual characters - which character of a group of possible characters is it.
There's a training command in the OCRopus that I had running for over a week on end, and it kept sending line samples to the map, slowly changing the map as it went. I think it was training the ANN part.
Yes, I'm aware that speech recognition is fairly complicated (as an understatement). What I'm looking for is a method for distinguishing between maybe 20-30 phrases. An ability to split words (discrete speech is fine) would be nice, but isn't required. The software will be user-dependent(i.e. for use by me). I'm not looking for existing software, but for a good way of going about doing this myself. I've looked into various existing methods and it seems like splitting the sound into phonemes, while common, is somewhat excessive for my needs.
For some context, I'm just looking for a way to control some aspects of my computer with a few simple voice commands. I'm aware that Windows already has speech recognition software, but I'd like to go about this one myself as a learning exercise. Commands would be simple like "Open Google", or "Mute". What I had in mind (not sure if this is a good idea) is that some commands would be compound. So "Mute" would just be "Mute". Whereas the "Open" command could be recognized individually, and then have its suffixes (Google, Photoshop, etc). recognized with another network/model/whatever. But I'm not sure if looking for prefixes/word breaks in this way would produce better results than having to deal with an increased number of individual commands.
I've been looking into perceptrons, hopfield networks (though they're somewhat obsolete from what I understand) and HMMs, and while I understand the ideas behind these (I've implemented the ANNs before) I don't really know which is best suited to this task. I'm assuming that linear vector quantization models would also be appropriate, but I can't really find much literature to this end. Any guidance/resources would be greatly appreciated.
There are some open source project in speech recognition:
HTK (Hidden Markov Models Toolkit)
Sphinx
Both have decoder, training, language model toolkits. Eveything to build a complete and robust speech recognizer.
Voxforge has acoustic and language models for both open source speech recognition toolkits.
Some time ago, I read a whitepaper about a limited vocabulary system, which used a simple recognition process. The system divided each utterance into a small number of bins (6 in time, and 4 in magnitude, if I remember correctly, for 24 total), and all it did was count the number of sample audio measurements in each bin. There was a fuzzy logic rule base which then interpreted each utterances 24 bin counts, and generated an interpretation.
I imagine that (for some applications) a simple matching process might work just as well, in which the 24 bin counts of the current utterance are simple matched against those of each of your stored prototypes, and the one with the least overall difference is the winner.
so essentially I have the task to learn matlab decently well in the next few weeks, and I want to really be able to impress the people i'm learning it for, so if you guys have advice, I would greatly appreciate it.
What I'm dealing with Throughout the course of my job I will be dealing largely in two areas.
Formatting and importing data from excel
Interpreting the data in a meaningful way.
I realize the second category is extremely broad, but essentially i'm working with arrays of pricing information to set some standard price for a commodity, so the analysis that I will be doing will be less computationally heavy and will focus more on error getting the data in correctly and accurately and making sure all of the functions that I want to perform are executed correctly. Very basic stuff.
So here's what I'm looking for:
What are the most important topics so that I can import data from excel into matlab perfectly?
What should I study just to get basic functions down that will be applied to entire matrices/arrays?
What should I study prior to studying anything else to get a firm foundation in the subject? (So I don't make stupid syntax errors, etc.)?
Your help is greatly appreciated :)
The first two things you'll want to learn about are (1) the Matlab GUI and (2) the basics of Matlab syntax. A quick visit to the Getting Started section of the Matlab documentation will do you wonders. It should take you less than a day to get through the basics. Do note that the Matlab documentation is generally excellent, and you should use it often.
Beyond that, there are a number of questions here about importing Excel into Matlab (importing from excel, exporting to excel), and there's some excellent docs on the Mathworks site as well (xlsread, xlswrite). Read them and become familiar with the details and common problems. Good luck!
Since you will be using excel it is likely the data you import could come out in cell format. Look into the differences between indexing using matrices and cells. Also, learn early on the difference between [operator] and .[operator] for vector calculations. The second one is element-wise and will most likely solve a lot of issues you'll come across if your records are meant to only relate to one row/column of data
Perhaps you have gained the MATLAB skills you need now, but hopefully this will help someone following the same path later.
The Math Works (developers of MATLAB) run a site called MATLAB Cody.
This site offers a range of problems and a web based MATLAB interpreter so you can test your solutions. When you have a correct solution, you can compare with other solutions to the same problem. Then you can look up the functions others have used in the online MATLAB documentation for more understanding.
The problems focus on regular expressions and cell manipulation which will be very relevant to importing and manipulating Excel data.
I'm trying to write a program in iPhone than can take two audio files (e.g. WAV) as inputs, compare them, and spit out a number that tells you how similar the audio files are.
If someone has done something like this, know how to go about doing it, or just have some ideas, please let me know. Anything will be greatly appreciated.
Specific questions: What language is suitable? How hard is it to do (how many
hours, roughly)? Where can I find a good source of audio library/tools?
Thanks!
I'd say it's pretty hard, not so much the implementation, but coming up with a reasonable definition of 'similar'.
That said, you're probably looking at techniques like autocorrelation and FFT, both of which are CPU-intensive tasks, so I'd say a fully-compiled language (C, C++, don't know about Objective-C) would be most suitable at least for the actual calculations. Also, you're facing a somewhat underpowered platform for such tasks (if only because uncompressed audio files are pretty large), so you're in for quite some optimization.
This book: http://www.dspguide.com/ is quite concise reading for all things DSP-related.
Sounds similar to what 'Shazam' does - awesome iPhone app by the way, check it out if you haven't already (it's free too).
A while ago there was an article on how Shazam works, read it here. It takes an acoustic fingerprint and compares it to other songs' fingerprints, returning the closest match.
I would say there is a lot of math, probably some matrices and maybe Fourier transforms involved in fingerprinting and then trying to compare the audio.
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Probably would take a good while to program. If your math skills are up to it though, sounds like a good challenge :-)
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EDIT: turns out there was some source code on the site I linked. It's in Java but would be well worth a look through before you start writing your own. Source code here
I am working on something similar in Java on a speech recognition app.
I would recommend using MFCC (requires calculating FFT) for feature extraction and Neural Networks or some other sort of machine learning technique for training and recognition. You train the NN with the features extracted from the reference wav file, more precisely from consecutive equal lenght slices/windows of that audio file. Then you use the NN to detect if another file, also split into slices, has the same features.
This is the basic idea upon which you can elaborate to further your own specifications, or exactly what you want your app to do.
In terms of libraries in Objective C I think you can find a few for the signal processing part (FFT and such) as for the machine learning part I have no idea about what you could find.
As for programming time it's hard to estimate because it depends on a lot of details. I would say somewhere about a week, but that's just a fair estimation.
ps: MFCC stands for Mel-Frequency Coeficients: http://en.wikipedia.org/wiki/Mel-frequency_cepstrum