| package query_builder | |
| import ( | |
| "strings" | |
| "strconv" | |
| "html/template" | |
| "fmt" | |
| ) | |
| type DynamicQueryBuilder string |
Functional programming is attracting interest from a broad range of developers because it allows to write expressive, concise and elegant programs.
The course "Introduction to Functional programming using the OCaml language" introduces gradually the central notions of functional programming, via a set of video courses that are complemented by a
Neural Style Transfer is the process of using Deep Neural Networks to migrate the semantic content of one image to different styles.
This gist implements NST in Owl, and provides a simple interfaces to use. Here is an example:
#zoo "6f28d54e69d1a19c1819f52c5b16c1a1"
| from selenium import webdriver | |
| from selenium.webdriver.support.ui import WebDriverWait | |
| from selenium.webdriver.support import expected_conditions as EC | |
| from selenium.webdriver.common.by import By | |
| from selenium.webdriver.common.keys import Keys | |
| def message(to,message=''): | |
| """this a simple function to send a whatsapp message to your friends | |
| and group using python and selenium an automated tool to parse the HTML | |
| content and to change the properties. |
| // To mock globally in all your tests, add to setupTestFrameworkScriptFile in config: | |
| // https://facebook.github.io/jest/docs/en/configuration.html#setuptestframeworkscriptfile-string | |
| jest.mock('moment', () => { | |
| const moment = require.requireActual('moment-timezone'); | |
| moment.tz.setDefault('America/Los_Angeles'); // Whatever timezone you want | |
| return moment; | |
| }); |
InceptionV3 is one of Google’s latest effort to do image recognition. This is a standard task in computer vision, where models try to classify entire images into 1000 classes, like "Zebra", "Dalmatian", and "Dishwasher". Compared with previous DNN models, InceptionV3 has one of the most complex networks architectures in computer vision models. The original paper of this network is here.
This gist implements an InceptionV3 service in Owl, and provides simple interfaces to use. Here is an example:
#zoo "9428a62a31dbea75511882ab8218076f"| #!/usr/bin/env bash | |
| #################################################################################### | |
| # Slack Bash console script for sending messages. | |
| #################################################################################### | |
| # Installation | |
| # $ curl -s https://gist.githubusercontent.com/andkirby/67a774513215d7ba06384186dd441d9e/raw --output /usr/bin/slack | |
| # $ chmod +x /usr/bin/slack | |
| #################################################################################### | |
| # USAGE | |
| # Send message to slack channel/user |
TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:
After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.
| mkdir -p /work/ | |
| # Clone the source from Github | |
| cd /work/ && git clone — recurse-submodules https://github.com/tensorflow/serving | |
| # Pin the version of Tensorflow Serving and its submodule | |
| TENSOR_SERVING_COMMIT_HASH=85db9d3 | |
| TENSORFLOW_COMMIT_HASH=dbe5e17 | |
| cd /work/serving && git checkout $TENSOR_SERVING_COMMIT_HASH |
| import sys | |
| import logging | |
| import logging.config | |
| from flask import Flask | |
| class LoggerConfig: | |
| dictConfig = { | |
| 'version': 1, | |
| 'disable_existing_loggers': False, | |
| 'formatters': { |
