Google Maps Updated 2025-07-16
Owned/developed by Google as of 2020.
Early on jumpstarted from several acquisitions, notably Keyhole Inc. and Where 2 Technologies.
JPEG Updated 2025-07-16
Sic Updated 2025-07-16
Bible Updated 2025-07-16
Big companies like Google are able to publish white papers as peer reviewed papers just due to their reputation, e.g. without giving any source code that is central for the article.
It is insane.
E.g.: AlphaGo is closed source but published as www.nature.com/articles/natnure16961 in 2016 on Nature.
If is the change of basis matrix, then the matrix representation of a bilinear form that looked like:
then the matrix in the new basis is:
Sylvester's law of inertia then tells us that the number of positive, negative and 0 eigenvalues of both of those matrices is the same.
Proof: the value of a given bilinear form cannot change due to a change of basis, since the bilinear form is just a function, and does not depend on the choice of basis. The only thing that change is the matrix representation of the form. Therefore, we must have:
and in the new basis:
and so since:
Q Updated 2025-07-16
R Updated 2025-07-16
S Updated 2025-07-16
ThinkPad Updated 2025-07-16
This is Ciro Santilli's favorite laptop brand. He's been on it since the early 2010's after he saw his then-girlfriend-later-wife using it.
Ciro doesn't know how to explain it, but ThinkPads just feel... right. The screen, the keyboard, the lid, the touchpad are all exactly what Ciro likes.
The only problem with ThinkPad is that it is owned by Lenovo which is a Chinese company, and that makes Ciro feel bad. But he likes it too much to quit... what to do?
Ciro is also reassured to see that in every enterprise he's been so far as of 2020, ThinkPads are very dominant. And the same when you see internal videos from other big tech enterprises, all those nerds are running... Ubuntu on ThinkPads! And the ISS.
Those nerds like their ThinkPads so much, that Ciro has seen some acquaintances with crazy old ThinkPad machines, missing keyboard buttons or the like. They just like their machines that much.
ThinkPads are are also designed for repairability, and it is easy to buy replacement parts, and there are OEM part replacement video tutorials: www.youtube.com/watch?v=vseFzFFz8lY No visible planned obsolescence here! With the caveat that the official online part stores can be shit as mentioned at Section "Lenovo".
The only thing Ciro never understood is the trackpoint: superuser.com/questions/225059/how-to-get-used-of-trackpoint-on-a-thinkpad Why would you use that with such an amazing touchpad? And vimium.
activatedgeek/LeNet-5 run on GPU Updated 2025-07-16
By default, the setup runs on CPU only, not GPU, as could be seen by running htop. But by the magic of PyTorch, modifying the program to run on the GPU is trivial:
cat << EOF | patch
diff --git a/run.py b/run.py
index 104d363..20072d1 100644
--- a/run.py
+++ b/run.py
@@ -24,7 +24,8 @@ data_test = MNIST('./data/mnist',
 data_train_loader = DataLoader(data_train, batch_size=256, shuffle=True, num_workers=8)
 data_test_loader = DataLoader(data_test, batch_size=1024, num_workers=8)

-net = LeNet5()
+device = 'cuda'
+net = LeNet5().to(device)
 criterion = nn.CrossEntropyLoss()
 optimizer = optim.Adam(net.parameters(), lr=2e-3)

@@ -43,6 +44,8 @@ def train(epoch):
     net.train()
     loss_list, batch_list = [], []
     for i, (images, labels) in enumerate(data_train_loader):
+        labels = labels.to(device)
+        images = images.to(device)
         optimizer.zero_grad()

         output = net(images)
@@ -71,6 +74,8 @@ def test():
     total_correct = 0
     avg_loss = 0.0
     for i, (images, labels) in enumerate(data_test_loader):
+        labels = labels.to(device)
+        images = images.to(device)
         output = net(images)
         avg_loss += criterion(output, labels).sum()
         pred = output.detach().max(1)[1]
@@ -84,7 +89,7 @@ def train_and_test(epoch):
     train(epoch)
     test()

-    dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True)
+    dummy_input = torch.randn(1, 1, 32, 32, requires_grad=True).to(device)
     torch.onnx.export(net, dummy_input, "lenet.onnx")

     onnx_model = onnx.load("lenet.onnx")
EOF
and leads to a faster runtime, with less user as now we are spending more time on the GPU than CPU:
real    1m27.829s
user    4m37.266s
sys     0m27.562s
Bilibili Updated 2025-07-16
Semi-boring academic overview, but without reproducibility, or in a way that is too hidden for Ciro to have the patience to find it out.
Claims 1600 files found.
Mentions some upload mechanisms, notably AtomSea & EMBII and Satoshi uploader.
Cursive script (East Asia) Updated 2025-07-16
As if Chinese character weren't evil enough, their fast hand written form is even more unintelligible. It is like Hell within Hell.
It is also very beautiful it must be said.
Eigendecomposition of a matrix Updated 2025-07-16
Every invertible matrix can be written as:
where:
Note therefore that this decomposition is unique up to swapping the order of eigenvectors. We could fix a canonical form by sorting eigenvectors from smallest to largest in the case of a real number.
Intuitively, Note that this is just the change of basis formula, and so:

There are unlisted articles, also show them or only show them.