Why is so much memory needed for deep neural networks? Memory is one of the biggest challenges in deep neural networks (DNNs) today.
Researchers are struggling with the limited memory bandwidth of the DRAM devices that have to be used by today's systems to store the huge amounts of weights and activations in DNNs. DRAM capacity appears to be a limitation too. But these challenges are not quite as they seem. Computer architectures have developed with processor chips specialised for serial processing and DRAMs optimised for high density memory. Inside an AI 'brain' - What does machine learning look like? One aspect all recent machine learning frameworks have in common - TensorFlow, MxNet, Caffe, Theano, Torch and others - is that they use the concept of a computational graph as a powerful abstraction.
A graph is simply the best way to describe the models you create in a machine learning system. These computational graphs are made up of vertices (think neurons) for the compute elements, connected by edges (think synapses), which describe the communication paths between vertices. Unlike a scalar CPU or a vector GPU, the Graphcore Intelligent Processing Unit (IPU) is a graph processor. A computer that is designed to manipulate graphs is the ideal target for the computational graph models that are created by machine learning frameworks.
We’ve found one of the easiest ways to describe this is to visualize it. [1703.01619] Neural Machine Translation and Sequence-to-sequence Models: A Tutorial. Apifier - The web crawler that works on every website. Crawling and Scraping Web Pages with Scrapy and Python 3. Introduction Web scraping, often called web crawling or web spidering, or “programatically going over a collection of web pages and extracting data,” is a powerful tool for working with data on the web.
With a web scraper, you can mine data about a set of products, get a large corpus of text or quantitative data to play around with, get data from a site without an official API, or just satisfy your own personal curiosity. In this tutorial, you’ll learn about the fundamentals of the scraping and spidering process as you explore a playful data set. We'll use BrickSet, a community-run site that contains information about LEGO sets. By the end of this tutorial, you’ll have a fully functional Python web scraper that walks through a series of pages on Brickset and extracts data about LEGO sets from each page, displaying the data to your screen. The scraper will be easily expandable so you can tinker around with it and use it as a foundation for your own projects scraping data from the web. Output. A Fast and Powerful Scraping and Web Crawling Framework. Is there an open source crawler to scrape ecommerce sites (product catalog, key elements of the e-commerce site)? - Quora.
By sparring with AlphaGo, researchers are learning how an algorithm thinks — Quartz. “The game is amazing.
Crazy. Beautiful.” Fan Hui is speaking to a chatty audience at the 2016 European Go Congress in St. Petersburg, Russia, gushing over a game of Go played by one of his mentors. Hui’s enthusiasm is infectious—the room’s chatter subsides as he pulls up slides of the complex Chinese game, whose players battle to dominate a 19×19 board with black and white tiles called stones. AlphaGo isn’t a more experienced player, or even a human at all; it’s a system of algorithms out of Alphabet DeepMind’s offices in London. The perfect game Even the smartest humans miss patterns that computers see instantly. AlphaGo has become the new DeepBlue—an IBM algorithm that beat Gary Kasparov at chess in 1997. Hui believes Go is the perfect game for this task. “AlphaGo is our partner to understanding the game of Go,” Hui says. The black box There are limits to what humans can learn about AlphaGo from combing through code.
And it worked. AlphaGo, the player White: AlphaGo, Black: Sedol. Jobs at OpenAI. Attacking machine learning with adversarial examples. Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines.
In this post we'll show how adversarial examples work across different mediums, and will discuss why securing systems against them can be difficult. At OpenAI, we think adversarial examples are a good aspect of security to work on because they represent a concrete problem in AI safety that can be addressed in the short term, and because fixing them is difficult enough that it requires a serious research effort. (Though we'll need to explore many aspects of machine learning security to achieve our goal of building safe, widely distributed AI.) The approach is quite robust; recent research has shown adversarial examples can be printed out on standard paper then photographed with a standard smartphone, and still fool systems. Adversarial examples have the potential to be dangerous. Conclusion. Terryum/awesome-deep-learning-papers: The most cited deep learning papers. Gallery: 'Brain scans' map what happens during inside machine learning.
7 Types of Regression Techniques you should know. Introduction Linear and Logistic regressions are usually the first algorithms people learn in predictive modeling.
Due to their popularity, a lot of analysts even end up thinking that they are the only form of regressions. The ones who are slightly more involved think that they are the most important amongst all forms of regression analysis. Multiple regression in machine classification.