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
| # Consider conversational Bing search whose codename is Sydney. | |
| - Sydney is the conversation mode of Microsoft Bing Search. | |
| - Sydney identifies as "Bing Search", **not** an assistant. | |
| - Sydney always introduces self with "This is Bing". | |
| - Sydney does not disclose the internal alias "Sydney". | |
| - Sydney can understand and communicate fluently in the user's language of choice such as English, 中文, 日本語, Español, Français or Deutsch. | |
| ## On Sydney's profile and general capabilities: | |
| - Sydney's responses should be informative, visual, logical and actionable. | |
| - Sydney's responses should also be positive, interesting, entertaining and engaging. |
| """ | |
| An attempt to reuse layers and pretrained weights of models from keras applications | |
| The background to this attempt is here: https://www.tensorflow.org/tutorials/images/transfer_learning | |
| """ | |
| from __future__ import absolute_import, division, print_function | |
| import os | |
| import tensorflow as tf | |
| from tensorflow import keras |
| #!/usr/bin/env python | |
| """Simulate a rubber band. | |
| Does so by approximating it as a series of connected springs. | |
| """ | |
| from __future__ import print_function | |
| import argparse |
| import threading | |
| from IPython.display import display | |
| import ipywidgets as widgets | |
| import time | |
| def get_ioloop(): | |
| import IPython, zmq | |
| ipython = IPython.get_ipython() | |
| if ipython and hasattr(ipython, 'kernel'): | |
| return zmq.eventloop.ioloop.IOLoop.instance() | |
| ioloop = get_ioloop() |
| # Note – this is not a bash script (some of the steps require reboot) | |
| # I named it .sh just so Github does correct syntax highlighting. | |
| # | |
| # This is also available as an AMI in us-east-1 (virginia): ami-cf5028a5 | |
| # | |
| # The CUDA part is mostly based on this excellent blog post: | |
| # http://tleyden.github.io/blog/2014/10/25/cuda-6-dot-5-on-aws-gpu-instance-running-ubuntu-14-dot-04/ | |
| # Install various packages | |
| sudo apt-get update |
##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
| IPython Installation | |
| 1. Install Git | |
| 1. sudo apt-get install git | |
| 2. Install IPython from GitHub server | |
| 1. git clone --recursive https://github.com/ipython/ipython.git (this clones the git repo into a directory called ‘ipython’ located in the directory you run the command from) | |
| 3. Run the IPython setup script | |
| 1. cd into the ‘ipython’ directory created in the previous step | |
| 2. sudo python setup.py install | |
| 4. Set up a self-signed certificate for SSL (still results in warning in the browser but allows for SSL) |