spaCy excels at large-scale information extraction tasks. It’s written from the ground up in carefully memory-managed Cython. Independent research has confirmed that spaCy is the fastest in the world. If your application needs to process entire web dumps, spaCy is the library you want to be using.
Folks tend to be curious about how much real projects cost to run on AWS, so here’s a real example with breakdowns by AWS service and feature.
This article walks through the AWS invoice for charges accrued in November 2016 by the TimerCheck.io API service which runs in the us-east-1 (Northern Virginia) region and uses the following AWS services:
- API Gateway
- AWS Lambda
- Route 53
- SNS (Simple Notification Service)
- CloudWatch Logs
- CloudWatch Metrics
- Network data transfer
- CloudWatch Alarms
During this month, TimerCheck.io service processed over 2 million API requests. Every request ran an AWS Lambda function that read from and/or wrote to a DynamoDB table.
Urn is a new language developed by SquidDev, and demhydraz. Urn is a Lisp dialect with a focus on minimalism which compiles to Lua.
- A minimal¹ Lisp implementation, with full support for compile time code execution and macros.
- Support for Lua 5.1, 5.2 and 5.3. Should also work with LuaJIT.
- Lisp-1 scoping rules (functions and data share the same namespace).
- Influenced by a whole range of Lisp implementations, including Common Lisp and Clojure.
- Produces standalone, optimised Lua files: no dependencies on a standard library.
¹: Minimalism is an implementation detail.
> Opening a webpage,
> Clicking on links,
> Modifying the content…
SlimerJS is useful to do functional tests, page automation, network monitoring, screen capture, web scraping etc.
SlimerJS is similar to PhantomJs, except that it runs on top of Gecko, the browser engine of Mozilla Firefox, instead of Webkit, and is not truly headless when running with Firefox 55 or older.
SlimerJS is compatible with CasperJS 1.1!!
Computer Vision has become ubiquitous in our society, with applications in search, image understanding, apps, mapping, medicine, drones, and self-driving cars. Core to many of these applications are visual recognition tasks such as image classification, localization and detection. Recent developments in neural network (aka “deep learning”) approaches have greatly advanced the performance of these state-of-the-art visual recognition systems. This course is a deep dive into details of the deep learning architectures with a focus on learning end-to-end models for these tasks, particularly image classification. During the 10-week course, students will learn to implement, train and debug their own neural networks and gain a detailed understanding of cutting-edge research in computer vision. The final assignment will involve training a multi-million parameter convolutional neural network and applying it on the largest image classification dataset (ImageNet). We will focus on teaching how to set up the problem of image recognition, the learning algorithms (e.g. backpropagation), practical engineering tricks for training and fine-tuning the networks and guide the students through hands-on assignments and a final course project. Much of the background and materials of this course will be drawn from the ImageNet Challenge.
… for, like, actual poets. By Allison Parrish
In this tutorial, I’m going to show you how word vectors work. This tutorial assumes a good amount of Python knowledge, but even if you’re not a Python expert, you should be able to follow along and make small changes to the examples without too much trouble.
This is a “Jupyter Notebook,” which consists of text and “cells” of code. After you’ve loaded the notebook, you can execute the code in a cell by highlighting it and hitting Ctrl+Enter. In general, you need to execute the cells from top to bottom, but you can usually run a cell more than once without messing anything up. Experiment!