This is incredible!
I wonder if seeing yourself dance like this might speed up learning to actually dance like this...
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E: alex [@@@] numberboost.com
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Consulting: www.numberboost.com
Here is a vid of one my talks on deep learning for computer vision: https://www.youtube.com/watch?v=X4Q6C915sUY
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I wonder if seeing yourself dance like this might speed up learning to actually dance like this...
I'm currently busy with my MSc thesis on learning to link plain text documents to semantically relevant Wikipedia articles (and some of the cool machine learning-y things you can do from there).
I have 2 questions about your work:
1. I'm not sure if you're familiar with Doc2Vec but it allows you to train a Word2Vec model while also learning a vector for each document in the training corpus. Wikipedia is commonly used as a training corpus so you can get a "DocVec" for each Wikipedia article in the same vector space as your Word2Vec model (i.e. the DocVec for the Wikipedia page "Machine Learning" is nearby the WordVec for "mathematics"). Did you consider/compare with using Doc2Vec to learn a vector for each Wikipedia page and then use those as your entity vectors?
2. Your "Features" page says you convert an entity name to a link pointing to an entity if the entity name is unambiguou". In the case of ambiguous entities from a link (which happens often - my research is only learning links to Wikipedia articles from plain text documents), did you consider using the entity vectors (or some simple model built on top of the word vectors of the target page) to disambiguate?
I find that talking it through a few times with someone will help you figure out which parts of your explanation are unclear and need rephrasing.
Also, don't take it too seriously - technical talks are already often dry - don't be afraid to insert some memes and jokes to keep the audience engaged!
Of course, you have to be alive to win so there's a survivorship bias but it'd be interesting to see a proper statistical analysis.
You can definitely find it online but be sure to find the right version - the latest version has great illustrations and is a lot clearer.
Also, check out the RL jupyter notebook here by my friend Ryan Sweke who does work on RL for quantum computing: https://github.com/R-Sweke/CrashCourseInNeuralNetworksWithKe...
SSN is a lookup key into the raw data. Dimensionality reduction is by definition lossy since it's used in scenarios where: rows of data = n <<< m = number of features
It's dimensionality reduction. You cannot recover the original object. It's like using a shadow to reconstruct the face of the person casting the shadow.
Note this has nothing to do with the expressive power of a deep neural network. You are by definition trying to throw away noisy aspects of the data and generalize a lower dimensional manifold from a high dimensional space. If it's not lossy, it won't generalize.
I'd strongly recommend checking out Peter Norvig's talk on "The Unreasonable Effectiveness of Big Data"
http://setosa.io/ev/image-kernels/
Highly recommended!
Not certain that's the correct intuition but it makes sense to me haha.
There are also many jobs that are demeaning or not intrinsically rewarding for people like trash collection and some kinda of construction. Enslave the computers so the humans can focus on higher minded pursuits.
A random forest automatically gives you a "variable importance plot".
There's so much hype about machine learning but so few people seem to appreciate the fundamental importance of linear algebra (especially in ML).
This fast.ai course is also great: http://www.fast.ai/2017/07/17/num-lin-alg/
I'd suggest anyone interested in jumping into building computer vision models should check out the excellent free MOOC on fast.ai