Classic Networks
LeNet-5: LeCun et al, "Gradient-Based Learning Applied to Document Recognition": http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf
AlexNet: Krizhevsky et al, "ImageNet Classification with Deep Convolutional Neural Networks": https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
VGG-16: Simonyan et al, "Very Deep Convolutional Networks for Large-Scale Image Recognition": https://arxiv.org/pdf/1409.1556.pdf
Resnets
| (Dijkstra and plain A* are generally not included here as there are thousands of | |
| implementations, though I've made an exception for rare Ruby and Crystal versions, | |
| and for Thor, Mapzen's enhanced A*. ) | |
| A* Ruby https://github.com/georgian-se/shortest-path | |
| A* Crystal https://github.com/petoem/a-star.cr | |
| A* (bidirectional with shortcuts) C++ https://github.com/valhalla/valhalla | |
| NBA* JS https://github.com/anvaka/ngraph.path | |
| NBA* Java https://github.com/coderodde/GraphSearchPal | |
| NBA* Java https://github.com/coderodde/FunkyPathfinding |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
from scipy.spatial import Delaunay, ConvexHull
import networkx as nx
points = [ [0,0],[0,50],[50,50],[50,0],[0,400],[0,450],[50,400],[50,450],[700,300],[700,350],[750,300],[750,350],
[900,600],[950,650],[950,600],[900,650]
]
def concave(points,alpha_x=150,alpha_y=250):
points = [(i[0],i[1]) if type(i) <> tuple else i for i in points]
de = Delaunay(points)
| #!/usr/bin/env python | |
| """Convert UnstructuredGrid in .vtk files to STL files.""" | |
| import sys | |
| import vtk | |
| if len(sys.argv) < 2 or sys.argv[1] == '-h' or sys.argv[1] == '--help': | |
| print('Usage: vtk-unstructuredgrid-to-stl.py <input.vtk> <output.stl>') | |
| sys.exit(1) |