I am a beginner in machine learning. I want to build a model for finding trending feeds like Instagram.
Please suggest which model is recommended for the same.
I will suggest you to choose these modeling frameworks like Modeling choices, Data freshness trading, and Novelty effect, Experimentation (A/B) Small Effects, Impact, and Scientific Method, Normalization, Iteration Speed — Offline Analysis, Value Modeling, and Parting Thoughts.
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As a part of a project, we want to do experiments with synthetic voices where these do not have a singular geographic origin, body, age or gender. We have our own data-set, but I thought of during initial experiments with VCTK and build a voice using Tacotron2 or something similar. Does anyone know if a similar project has been done? Where the physical body that we imagine connected to a voice is intentionally ambiguous. Or other projects where TTS has be trained on a multi-person corpus? Additionally, does anyone know of any caveats or potential problems in terms of this approach? Maybe there could be ways of working with transfer-learning that could be beneficial.
Thanks!
You can check https://github.com/r9y9/deepvoice3_pytorch
Multispeaker samples are available as well as pretrained model you can try.
I have a task: to determine the sound source location.
I had some experience working with tensorflow, creating predictions on some simple features and datasets. I assume that for this task, there would be necessary to analyze the sound frequences and probably other related data on training and then prediction steps. The sound goes from the headset, so human ear is able to detect the direction.
1) Did somebody already perform that? (unfortunately couldn't find any similar project)
2) What kind of caveats could I meet while trying to achieve that?
3) Am I able to do that using this technology approach? Are there any other sound processing frameworks / technologies / open source projects that could help me ?
I am asking that here, since my research on google, github, stackoverflow didn't show me any relevant results on that specific topic, so any help is highly appreciated!
This is typically done with more traditional DSP with multiple sensors. You might want to look into time difference of arrival(TDOA) and direction of arrival(DOA). Algorithms such as GCC-PHAT and MUSIC will be helpful.
Issues that you might encounter are: DOA accuracy is function of the direct to reverberant ratio of the source, i.e. the more reverberant the environment the harder it is to determine the source location.
Also you might want to consider the number of location dimensions you want to resolve. A point in 3D space is much more difficult than a direction relative to the sensors
Using ML as an approach to this is not entirely without merit but you will have to consider what it is you would be learning, i.e. you probably don't want to learn the test rooms reverberant properties but instead the sensors spatial properties.
There are a lot of programs that do parameter learning for Bayes nets. I am having a hard time finding libraries or tools that do (or try to do) structure learning. Specifically, one that uses an information theoretic approach, by looking at the information gain from adding an edge, or analyzing the cross entropy across Random Variables to determine if they have any relationships or are independent. This is not the core problem I am trying to work on, but learning structure is an important part of it. So finding an existing tool/library would help immensely.
Try the bnlearn library. It contains structure learning, parameter learning, inference and various well-known example datasets such as Sprinkler, Asia, Alarm.
Github documentation pages
Examples can be found here
Blog about detecting causal relationships can be found here.
I'm currently working on a project that requires a database categorising websites (e.g. cnn.com = news). We only require broad classifications - we don't need every single URL classified individually. We're talking to the usual vendors of such databases, but most quotes we've had back are quite expensive and often they impose annoying requirements - like having to use their SDKs to query the database.
In the meantime, I've also been exploring the possibility of building such a database myself. I realise that this is not a 5 minute job, so I'm doing plenty of research.
From reading various papers on the subject, it seems a Naive Bayes classifier is generally the standard approach for doing this. However, many of the papers suggest enhancements to improve its accuracy in web classification - typically by making use of other contextual information, such as hyperlinks, header tags, multi-word phrases, the URL, word frequency and so on.
I've been experimenting with Mahout's Naive Bayes classifier against the 20 Newsgroup test dataset, and I can see its applicability to website classification, but I'm concerned about its accuracy for my use case.
Is anyone aware of the feasibility of extending the Bayes classifier in Mahout to take into account additional attributes? Any pointers as to where to start would be much appreciated.
Alternatively, if I'm barking up entirely the wrong tree please let me know!
You can control the input about as much as you'd like. In the end the input is just a feature vector. The feature vector's features can be words, or bigrams -- but they can also be whatever you want. So, yes, you can inject new features by modifying the input as you like.
How best to weave in those features is another topic entirely -- there's not one best way to convert them to numbers. Mahout in Action covers this reasonably well FWIW.
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I'm looking for ideas for a Neural Networks project that I could complete in about a month or so. I'm doing it for the National Science Fair, so I need something that has some curb appeal as well since it's being judged.
It doesn't necessarily have to be completely new and unique, I'm just looking for ideas, but it should be complex enough that it would impress someone who knows about the field. My first idea was to implement a spam filter of sorts, but I recently found out that NN's aren't a very good way to do it. I've already got a basic NN simulator with Genetic Algorithms, and I'm also adding the the generic back-propagation algorithms as well.
Any ideas?
Look into Numenta's Hierarchical Temporal Memory (HTM) concept. This may be slightly off topic if the expectation is of "traditional" Neural Nets, but it is also an extremely promising avenue for Artificial Intelligence.
Although Numenta introduced HTM and its associated software platform, NuPIC, almost five years ago, the first commercial product based upon this technology was released (in beta) a few weeks ago by Vitamin D. It is called Vitamin D Video and essentially turns any webcam or IP camera into a sophisticated video monitoring system, recognizing classes of items (say persons vs. cats or other animals) in the video feed.
With the proper setup, this type of application could make for an interesting display at the Science Fair, one with much "curb appeal".
To wet your appetite or even get your feet wet with HTM technology you can download NuPIC and check its various sample applications. Chances are that you may find something that meets typical criteria of both geekness and coolness for science fairs.
Generally, HTMs aim at solving problems which are simple for humans but difficult for computers; such a statement is somewhat of a generic/applicable to Neural Nets, but HTMs take this to the "next level".
Although written in C (I think) NuPIC is typically interfaced in Python, which makes it a convenient test bed for simple yet sophisticated proofs of concept applications.
You could always try to play around with a neural network and stock courses, if I had a month of spare time for a neural network implementation, thats what I would play with.
A friend of mine in college wrote a NN to play go on a 9x9 board.
I don't think it ever got very good, but I think it would be fun to try.
Look on how a bidirectional associative memory compare with other classical edit distance algorithms (Levenshtein, Damerau-Levenshtein etc) for typo correction. Also consider the articles on hebbian unlearning while training your NN - it seems that the confabulation phenomena is avoided.
I've done some works on top of NN, mainly an XML based language (Neural XML). See details here
http://amazedsaint.blogspot.com/search/label/Neural%20Network
Also, one interesting .NET Neural network project is Aforge.net - Check out that as well..
You can implement the game Cellz or create a controller for it. It was first created by Simon M Lucas. It's a nice and interesting game, and i'm sure that everyone will love it. I used it also for a school project and it turned out very ok.
You can find in that page some links to other interesting games.
How about applying it to predicting exchange rate (USD - EUR for example for sub minute trading) should be fun to show net gain of money over 1 month.
I doubt this will work for trades longer than a minute... without a lot of extra work.
I like using committee machines so why not apply it to Face-Detection in images / movies or voice print authentication.
Finally you could get it to play pleasing music and use a crowd sourcing fitness function whereby people vote for the best "musicians"