duplicates = multiple editions
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
A Classical Introduction to Modern Number Theory, Kenneth Ireland Michael Rosen
| # Authors: Kyle Kastner, Francesco Visin | |
| # License: BSD 3-Clause | |
| try: | |
| import Queue | |
| except ImportError: | |
| import queue as Queue | |
| import threading | |
| import time | |
| from matplotlib.path import Path | |
| from PIL import Image |
| # Author: Kyle Kastner | |
| # License: BSD 3-Clause | |
| # For a reference on parallel processing in Python see tutorial by David Beazley | |
| # http://www.slideshare.net/dabeaz/an-introduction-to-python-concurrency | |
| # Loosely based on IBM example | |
| # http://www.ibm.com/developerworks/aix/library/au-threadingpython/ | |
| try: | |
| import Queue | |
| except ImportError: | |
| import queue as Queue |
A comparison of Theano with other deep learning frameworks, highlighting a series of low-level design choices in no particular order.
Overview
Symbolic: Theano, CGT; Automatic: Torch, MXNet
Symbolic and automatic differentiation are often confused or used interchangeably, although their implementations are significantly different.
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
| public static UIImage Blur(this UIImage image, float blurRadius = 25f) | |
| { | |
| if (image != null) | |
| { | |
| // Create a new blurred image. | |
| var imageToBlur = new CIImage (image); | |
| var blur = new CIGaussianBlur (); | |
| blur.Image = imageToBlur; | |
| blur.Radius = blurRadius; | |
| namespace FsharpImaging | |
| open System | |
| open System.Drawing | |
| open System.Drawing.Imaging | |
| open System.Runtime.InteropServices | |
| module ArrayFunctions = | |
| // Generic because it makes testing easier, yay math | |
| let IsSubMatrix (smallArray:'a[][]) (largeArray:'a[][]) (startCoordinate:(int * int)) = |
| import time | |
| import numpy as NP | |
| from redis import StrictRedis as redis | |
| # a 2D array to serialize | |
| A = 10 * NP.random.randn(10000).reshape(1000, 10) | |
| # flatten the 2D NumPy array and save it as a binary string | |
| array_dtype = str(A.dtype) |