Oleksii Gapchenko deus42

Installing Fish shell on MacOS (Intel and M1) using brew

Fish is a smart and user-friendly command line (like bash or zsh). This is how you can instal Fish on MacOS and make your default shell.

Note that you need the https://brew.sh/ package manager installed on your machine.

Install Fish

brew install fish

TensorFlow development environment on Windows using Docker

Here are instructions to set up TensorFlow dev environment on Docker if you are running Windows, and configure it so that you can access Jupyter Notebook from within the VM + edit files in your text editor of choice on your Windows machine.

Installation

First, install https://www.docker.com/docker-toolbox

Since this is Windows, creating the Docker group "docker" is not necessary.

##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

Comparison of ASP.NET and Node.js for Backend Programming

We will compare ASP.NET and Node.js for backend programming.
Source codes from examples.

Updates

This document was published on 21.09.2015 for a freelance employer. Some changes since then (14.02.2016):

Koa.js no longer uses co-routines, it has switched to Babel's async/await. yield and await are used almost in the same way, so I see no point to rewrite the examples.

	#!/bin/sh
	# See video https://www.youtube.com/watch?v=7PO27i2lEOs

	set -e


	command_exists () {
	type "$1" &> /dev/null ;
	}

	CREATE TABLE IF NOT EXISTS streamingInventoryEvents(
	locationType text,
	locationId int,
	cacheTime timestamp,
	itemNumber text,
	tag text,
	time timestamp,
	scanType int,
	pieceTag text,
	repId int,

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	'''This script goes along the blog post
	"Building powerful image classification models using very little data"
	from blog.keras.io.
	It uses data that can be downloaded at:
	https://www.kaggle.com/c/dogs-vs-cats/data
	In our setup, we:
	- created a data/ folder
	- created train/ and validation/ subfolders inside data/
	- created cats/ and dogs/ subfolders inside train/ and validation/
	- put the cat pictures index 0-999 in data/train/cats

	"""
	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)