x86 Paging Tutorial Why not a balanced tree Updated 2025-07-16
Learned readers will ask themselves: so why use an unbalanced tree instead of balanced one, which offers better asymptotic times en.wikipedia.org/wiki/Self-balancing_binary_search_tree?
Y Updated 2025-07-16
Z Updated 2025-07-16
2023 Nobel Prize in Chemistry Updated 2025-07-16
Cardano Updated 2025-07-16
Google Maps Updated 2025-07-16
Early on jumpstarted from several acquisitions, notably Keyhole Inc. and Where 2 Technologies.
JPEG Updated 2025-07-16
LaTeX to HTML Updated 2025-07-16
Sic Updated 2025-07-16
1955 Nobel Prize in Physics Updated 2025-07-16
The key initial quantum electrodynamics experiments:
BeyondResearch Updated 2025-07-16
Bible Updated 2025-07-16
Big companies manage to publish white papers in peer reviewed journals 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.
Effect of a change of basis on the matrix of a bilinear form Updated 2025-07-16
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:
Portable Network Graphics Updated 2025-07-16
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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".
Further more, in 2020 Lenovo is announced full certification for Ubuntu www.forbes.com/sites/jasonevangelho/2020/06/03/lenovos-massive-ubuntu-and-red-hat-announcement-levels-up-linux-in-2020/#28a8fd397ae0 which fantastic news!
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:and leads to a faster runtime, with less
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")
EOFuser as now we are spending more time on the GPU than CPU:real 1m27.829s
user 4m37.266s
sys 0m27.562s There are unlisted articles, also show them or only show them.