Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
---|---|---|---|---|---|---|---|---|---|---|
Deep Learning From Scratch 3 | 426 | 2 years ago | 5 | mit | Python | |||||
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020) | ||||||||||
Gmmn | 132 | 7 years ago | Python | |||||||
Generative moment matching networks | ||||||||||
Neural Gpu | 89 | 5 years ago | 1 | Python | ||||||
Code for the Neural GPU model originally described in "Neural GPUs Learn Algorithms" | ||||||||||
Gpugraphtx | 73 | 3 years ago | 2 | mit | Python | |||||
Simple moving graph of GPU activity for the Jetson TX1 and Jetson TX2 | ||||||||||
Neural Network Sandbox | 64 | a year ago | 13 | gpl-2.0 | Python | |||||
This is a source code of Software called Neural Network Sandbox.Neural Network Sandbox makes making and training basic feed forward neural networks easy. | ||||||||||
Image Classifier | 53 | 5 years ago | mit | Jupyter Notebook | ||||||
Final Project of the Udacity AI Programming with Python Nanodegree | ||||||||||
Gpugraph | 23 | 4 years ago | 1 | mit | Python | |||||
Simple moving graph of GPU activity for the Jetson Nano Developer Kit | ||||||||||
Anpr Tensorflow | 20 | 4 years ago | 5 | Python | ||||||
Using neural networks to build an automatic number plate recognition system. | ||||||||||
Gpu_mandelbrot | 15 | a year ago | mit | Python | ||||||
Interactive Mandelbrot set on GPU with Python | ||||||||||
Autoencoder | 8 | 6 years ago | mit | Python | ||||||
Autoencoder using Theano library |
from mandelbrot import Mandelbrot
mand = Mandelbrot() # set `gpu = False` if no GPU is available
# Explore the set using interactive Matplotlib GUI
mand.explore()
# Draw an image and save it to file
mand.draw('mandelbrot.png')
# We decrease the image size to avoid overloading git and the readme page
# We also increase the number of iterations, and add stripe coloring
mand = Mandelbrot(maxiter = 5000, xpixels = 426, stripe_s = 5)
# Point to zoom at
x_real = -1.749705768080503
x_imag = -6.13369029080495e-05
mand.animate(x_real, x_imag, 'mandelbrot.gif')
Use the following command from terminal to launch a GUI with default parameters:
python ./mandelbrot.py
Some examples of HD images, and corresponding code:
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.11, .02, .92], stripe_s = 2,
coord = [-0.5503295086752807,
-0.5503293049351449,
-0.6259346555912755,
-0.625934541001796])
mand.draw('crown.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.29, .02, 0.9], ncycle = 8,
step_s = 10,
coord = [-1.9854527029227764,
-1.9854527027615938,
0.00019009159314173224,
0.00019009168379912058])
mand.draw('pow.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.83, .01, .99], stripe_s = 5,
coord = [-1.749289287806423,
-1.7492892878054118,
-1.8709586016347623e-06,
-1.8709580332005737e-06])
mand.draw('octogone.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.87, .83, .77],
coord = [-1.9415524417847085,
-1.9415524394561112,
0.00013385928801614168,
0.00013386059768851223])
mand.draw('julia.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.54, .38, .35], stripe_s = 8,
coord = [-0.19569582393630502,
-0.19569331188751315,
1.1000276413181806,
1.10002905416902])
mand.draw('lightning.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.47, .51, .63], step_s = 20,
coord = [-1.7497082019887222,
-1.749708201971718,
-1.3693697170765535e-07,
-1.369274301311596e-07])
mand.draw('web.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.6, .57, .45], stripe_s = 12,
coord = [-1.8605721473418524,
-1.860572147340747,
-3.1800170324714687e-06,
-3.180016406837821e-06])
mand.draw('wave.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.63, .83, .98],
coord = [-0.7545217835886875,
-0.7544770820676441,
0.05716740181493137,
0.05719254327783547])
mand.draw('tiles.png')
mand = Mandelbrot(maxiter = 5000, rgb_thetas = [.29, .52, .59], stripe_s = 5,
coord = [-1.6241199193994318,
-1.624119919281773,
-0.00013088931048083944,
-0.0001308892443058033])
mand.draw('velvet.png')
Computing a sequence of 100
frames of HD pictures (1280*720
pixels), with 2000
iterations takes approximately 1 second on a Tesla K80 GPU.