Machine learning company Updated 2025-07-16
This section is about companies that primarily specialize in machine learning.
The term "machine learning company" is perhaps not great as it could be argued that any of the Big tech are leaders and sometimes, especially in the case of Google, has a main product that is arguably a form of machine learning.
Manifold Updated 2025-07-16
We map each point and a small enough neighbourhood of it to , so we can talk about the manifold points in terms of coordinates.
Does not require any further structure besides a consistent topological map. Notably, does not require metric nor an addition operation to make a vector space.
A notable example of a Non-Euclidean geometry manifold is the space of generalized coordinates of a Lagrangian. For example, in a problem such as the double pendulum, some of those generalized coordinates could be angles, which wrap around and thus are not euclidean.
OAuth Updated 2025-07-16
The fatal flaw of OAuth is that websites have to enable specific providers, they can't just automatically select the correct OAuth for a given email domain. This means that the vast majority of websites will only provide the most widely popular providers such as Google, and the like, which means people won't have decent privacy.
Order of an element of a group Updated 2025-07-16
Digital-to-analog converter Updated 2025-07-16
Electric motor Updated 2025-07-16
Polonium isotope Updated 2025-07-16
There are no stable isotopes.
Sony Updated 2025-07-16
Infinite dimensional Updated 2025-07-16
Multivariable chain rule Updated 2025-07-16
Nintendo Updated 2025-07-16
Ordinary differential equation Updated 2025-07-16
PostgreSQL spatial index Updated 2025-07-16
python/infer.py Updated 2025-07-16
SQL:1999 Updated 2025-07-16
Stack Overflow user Updated 2025-07-16
Ubuntu 22.04 Updated 2025-07-16
Ciro's Edict #7 Misc tech Updated 2025-07-16
Tileset Updated 2025-07-16
activatedgeek/LeNet-5 Updated 2025-07-16
It trains the LeNet-5 neural network on the MNIST dataset from scratch, and afterwards you can give it newly hand-written digits 0 to 9 and it will hopefully recognize the digit for you.
Ciro Santilli created a small fork of this repo at lenet adding better automation for:
- extracting MNIST images as PNG
- ONNX CLI inference taking any image files as input
- a Python
tkinter
GUI that lets you draw and see inference live - running on GPU
Install on Ubuntu 24.10 with:We use our own
sudo apt install protobuf-compiler
git clone https://github.com/activatedgeek/LeNet-5
cd LeNet-5
git checkout 95b55a838f9d90536fd3b303cede12cf8b5da47f
virtualenv -p python3 .venv
. .venv/bin/activate
pip install \
Pillow==6.2.0 \
numpy==1.24.2 \
onnx==1.13.1 \
torch==2.0.0 \
torchvision==0.15.1 \
visdom==0.2.4 \
;
pip install
because their requirements.txt uses >=
instead of ==
making it random if things will work or not.On Ubuntu 22.10 it was instead:
pip install
Pillow==6.2.0 \
numpy==1.26.4 \
onnx==1.17.0 torch==2.6.0 \
torchvision==0.21.0 \
visdom==0.2.4 \
;
Then run with:This script:
python run.py
It throws a billion exceptions because we didn't start the Visdom server, but everything works nevertheless, we just don't get a visualization of the training.
The terminal outputs lines such as:
Train - Epoch 1, Batch: 0, Loss: 2.311587
Train - Epoch 1, Batch: 10, Loss: 2.067062
Train - Epoch 1, Batch: 20, Loss: 0.959845
...
Train - Epoch 1, Batch: 230, Loss: 0.071796
Test Avg. Loss: 0.000112, Accuracy: 0.967500
...
Train - Epoch 15, Batch: 230, Loss: 0.010040
Test Avg. Loss: 0.000038, Accuracy: 0.989300
One of the benefits of the ONNX output is that we can nicely visualize the neural network on Netron:
Netron visualization of the activatedgeek/LeNet-5 ONNX output
. From this we can see the bifurcation on the computational graph as done in the code at:output = self.c1(img)
x = self.c2_1(output)
output = self.c2_2(output)
output += x
output = self.c3(output)
There are unlisted articles, also show them or only show them.