In Search Engine Optimization All-in-One For Dummies, 3rd Edition, Bruce Clay--whose search engine consultancy predates Google--shares everything you need to know about SEO. In minibooks that cover the entire topic, you'll discover how search engines work, how to apply effective keyword strategies, ways to use SEO to position yourself competitively, the latest on international SEO practices, and more. If SEO makes your head spin, this no-nonsense guide makes it easier. You'll get the lowdown on how to use search engine optimization to improve the quality and volume of traffic on your website via search engine results. Cutting through technical jargon, it gets you up to speed quickly on how to use SEO to get your website in the top of the rankings, target different kinds of searches, and win more industry-specific vertical search engine results!
Helpful installation and setup instructions can be found in the README.md To access the code materials for a given chapter, simply click on the open dir links next to the chapter headlines to navigate to the chapter subdirectories located in the code/ subdirectory. You can also click on the ipynb links below to open and view the Jupyter notebook of each chapter directly on GitHub. In addition, the code/ subdirectories also contain .py However, I highly recommend working with the Jupyter notebook if possible in your computing environment.
This post is a simplified yet in-depth guide to word2vec. In this article, we will implement word2vec model from scratch and see how embedding help to find similar/dissimilar words. Word2Vec is the foundation of NLP( Natural Language Processing). Tomas Mikolov and the team of researchers developed the technique in 2013 at Google. Their approach first published in the paper'Efficient Estimation of Word Representations in Vector Space'.
What was humanity's first invention? Some say it was the wheel, while others say it was fire. But perhaps it was our invention of communication. Without this, no tool can be conceptualized, built, replicated, and improved upon by others over time. Over the years, how we communicate has evolved immensely.
The technology sector is set to benefit from a £18.5 million cash injection to drive up skills in AI and data science and support more adults to upskill and retrain to progress in their careers or find new employment. Up to 2,500 people will have the opportunity to retrain and become experts in data science and artificial intelligence (AI), thanks to a £13.5 million investment to fund new degree and Masters conversion courses and scholarships at UK academic institutions over the next three years. The ground-breaking Adult Learning Technology Innovation Fund, which will be launched in partnership with innovation foundation Nesta, will provide funding and expertise to incentivise tech firms to harness new technologies to develop bespoke, flexible, inclusive, and engaging online training opportunities to support more people into skilled employment. Companies across the tech sector already employ more than 2.1 million people, contribute £184 billion to the economy every year and inward investment to the UK AI sector stood at £1 billion for 2018, which is more than Germany, France, Netherlands, Sweden and Switzerland combined. To further strengthen the sector, Government is investing in data-driven technologies, such as artificial intelligence, through the modern Industrial Strategy, so tech businesses and people with the drive and talent can succeed.
Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants. The artificial intelligence studies footage of plants near the line taken from trains and attempts to spot when leaves change colour, indicating that they might fall. It can also warn of fallen trees and when vegetation growth might soon obstruct the path of trains and lead to delays. The project is one of 24 high-tech schemes that have today been funded a total of £7.8 million ($9.9 million) by the UK government to improve the nation's railways. Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants (stock image) Slippery rails -- commonly referred to as'leaves on the line' -- result when build ups on the track led to trains not being able to grip the rails properly.
In his new novel, Machines Like Me, the novelist Ian McEwan tells the story, set in an alternate history in England in 1982, of a man who buys a humanoid robot. This triumph of artificial intelligence is "a creation myth made real," he writes, but also "a monstrous act of self-love." Part companion and part servant, the robot named Adam is "the ultimate plaything, the dream of ages, the triumph of humanism -- or its angel of death." One of the first things Adam says when he is switched on is "I don't feel right," and, typically for cautionary tales about robots, it only gets worse from there. Like much of the frontier thinking on the morality of artificial intelligence, McEwan's unsettling vision comes from fiction, not science.
Artificial intelligence (AI) could provide a 22 percent boost to the UK's GDP by 2030 according to new research from McKinsey and Quantumblack. The research also revealed that British companies which fully incorporate AI tools into their organizations could increase their economic value by 120 percent by 2030. However, organizations that are late to adopt AI or fail to do so all together, could lose around 20 percent of their cash flow compared to today. Despite the fact that the UK is ahead of the rest of Europe on McKinsey Global Institute's AI readiness index, it continues to fall behind the US and China. McKinsey and Quantumblack's research also suggests that increased AI adoption would be a "welcome boost" in the UK where productivity growth has been weak over the last decade.