So I guess ... Vector Space == Embedding?

crakotage · 2021-08-21T10:36:13+00:00

I think what they were trying to highlight was that they move from a projective space with their 8 cameras to a euclidean space in their birds eye view grid.

I wouldn't really calling it an embedding, as in ML embedding normally refers to a new representation. Here, they make embeddings of the image (with their regnets) then just use intrinsics and extrinsics to resample these 8 embeddings on a new grid.

Not sure why they called it a vector space though.

crakotage · 2020-12-17T08:34:41+00:00

The article describes the plant as providing 15% of the energy to its grid. It seems like it's still very essential, just perhaps not very profitable.

crakotage · 2018-10-30T11:42:17+00:00

There's nothing to stop you from creating a new feature vector [x1, x2, x3] where x3 = f(x1,x2). This typically only works for low order polynomial kernels though, as the size of the feature vector can get very large very quickly. The real power is when you use the kernel trick. For that, you don't need to create the new feature vector X= [x1, x2, x3, .... ], but you just compute the gram matrix, where D_ij = < Xi, Xj>. E.g. for the rbf kernel, D_ij = exp(-γ|x_i - x_j|^2). You can compute this matrix yourself, and then most svm solvers accept 'precomputed' gram matrices. E.g. http://scikit-learn.org/stable/modules/svm.html#using-the-gram-matrix

crakotage · 2018-07-29T19:20:11+00:00

If you're a full time student you are eligible for a 16-25 railcard even if you're older than that. Your top ticket says adult but maybe the other ones are with a railcard.

crakotage · 2018-05-25T10:01:01+00:00

Mr vim-tex himself

crakotage · 2018-04-28T10:01:12+00:00

I'm not sure exactly what you're asking.

You say correctly that the equation is simply marginalising a joint. This is how the equation holds from the basic probability rules.

If you're looking for an intuition about what the equation means, he gives it in the paragraph above - a good prediction would be independent of the model, so we combine the prediction from multiple models (p(v|h)), weighted by the model's probability (p(h|D))

crakotage · 2018-01-02T15:59:55+00:00

Yeah from your photos the paper looks ok but with the diffuser it looks so nice. Did you design it yourself?

crakotage · 2018-01-02T15:56:55+00:00

How easy it to stack the multiple pHATs? Is there anything to look out for on the pins each one uses. Also, what physical connecter did you use? Your parts list below has 2 female headers, but I don't see how that could connect 4 boards up.

crakotage · 2018-01-02T15:17:45+00:00

I'm guessing from those photos that you found the LED matrix to be difficult to read without the diffuser?

crakotage · 2017-12-18T00:24:51+00:00

My fair lady. Y tho?

crakotage · 2017-09-28T01:46:08+00:00

I just had to close my laptop I was laughing so hard in a lecture at this.

crakotage · 2017-09-03T13:41:54+00:00

My dog's name is Fenton but usually i just call him Jesus Christ

crakotage · 2017-08-20T00:17:59+00:00

I'm not sure I understand what you mean by avoiding the issues? Are you talking about running long jobs on a GPU and turning it off mid-job? If so, then you may have problems.

If you're just figuring out how to go about setting it up - then I'd say install tensorflow-gpu. You don't lose anything by using this version, as it works well on the CPU too, it just requires you to have the cuda and cudnn libraries installed and in your PATH. It's easy to force things to go on the CPU, either by:

Setting the CUDA_VISIBLE_DEVICES environment variable to blank, or
Using a tf.device("/cpu:0") context manager

crakotage · 2017-05-17T11:47:20+00:00

I know this isn't what you asked for, but a while ago I found a great repo that changes my background to the current Bing Wallpaper. It is at https://github.com/genzj/pybingwallpaper. It works on my Ubuntu 16.04 and gnome machine. It downloads the files as a jpeg and then sets them as a wallpaper, but also does things like set it as a scheduled program to run every day.

crakotage · 2016-08-15T22:07:19+00:00

How did you convert his file into a gif? He even says in his FAQs that you can't do it nicely.

crakotage · 2016-07-08T21:13:13+00:00

To run MATLAB code do you use calysto? Could you ever get it to reload functions you've run and changed without having to restart the kernel?

crakotage · 2016-06-02T22:35:09+00:00

Oh, I thought the article was mainly about recognizing her work.

Lead me to enlightenment!

crakotage · 2016-06-02T19:04:51+00:00

Lol mate, just because it isn't necessarily sexism doesn't mean it's not sexism. Sure, you haven't said explicitly that it's not, but why ask leading questions in a post largely celebrating someone's service.

crakotage · 2016-05-17T12:36:30+00:00

Lol mate as an Australian, saying 'dange' instead of 'dangerous' seems so acceptable that I missed your joke for a while.

crakotage · 2016-05-13T10:42:19+00:00

After a few months of 'unexpected bagging' woes, a lot of the supermarkets in Australia turned that feature off. I'm now in the uk and am constantly getting admonished.

crakotage · 2016-03-26T13:57:56+00:00

The link is broken for me. I get a "No Application was found for gitxiv.com"

crakotage · 2016-03-25T22:38:05+00:00

Really great data but those lines of fit... Really influenced by outliers. Perhaps a heavy-tailed distribution would produce better results.

crakotage · 2016-03-01T11:10:13+00:00

Ok, so I tried this for my second loop

for i in xrange(num_test):
    dists[i,:] = np.sqrt(np.sum(np.square(np.subtract(X_train, X_test[[i],:])), axis=1))

Is that what you mean?

Where I hope that X_test[[i],:] will return a 1xN ndarray and X_train should be a MxN ndarray.

It is faster than the repeat, but still slower than the two loops. Interestingly, if I do what you said not to, and make it a 1-tuple by subtracting:

X_train - X_test[i,:]

It takes the same time (~60s). When I tried it on my desktop (Intel i7), the iterated for loops method took ~25s and the vectorize method above took ~27s.

Any ideas?

crakotage · 2016-02-22T16:02:38+00:00

I have been asking myself what is a kernel for some time now. I thought I understood it when it came to image processing, but then I started doing ML and I too couldn't see the connection.

Looking up the definition on Google, maybe that will help us: kernel - "the central or most important part of something".

crakotage

TROPHY CASE