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Data Analyst

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As a Product Data Analyst (f/m) you will partner with our product teams to help measure and understand the value we deliver to our customers. Your insights will be key to enable data driven decision-making of Product Development at Contentful. It lets developers and content creators work in parallel, increasing team efficiency and happiness. Companies such as Co-op, Spotify, Bang&Olufson, N26, Swarovski use Contentful to build their mobile and web products, voice controlled apps and more. We're growing rapidly and are backed by over $150 million in funding from top-tier venture capital firms like Sapphire Ventures, Salesforce Ventures, General Catalyst and Benchmark.


Virtus AllianzGI Artificial Intelligence & Technology Opportunities Fd (AIO) falls -0.9700% for June 18

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Virtus AllianzGI Artificial Intelligence & Technology Opportunities Fd (NYSE: AIO) shares fell -0.9700% to end trading Friday at $27.37 per share - a net change of $-0.268. Shares traded between $27.55 and $27.30 throughout the day. The Fund seeks to generate a stable income stream and growth of capital by focusing on one of the most significant long-term secular growth opportunities in markets today. A multi-asset approach based on fundamental research is employed, dynamically allocating to attractive segments of a company's debt and equity in order to offer an attractive risk/reward profile. Innovators and Disruptors -- The Fund invests in a growing universe of opportunities across a broad spectrum of technologies and sectors embracing the disruptive power of artificial intelligence and other new technologies.


Machine Learning Engineer

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This can be demonstrated by a track record of happy, referenceable customers who appreciate your technical acumen and your diligence. This is a hands-on role – be ready to jump in and use the product from your first day. We would be thrilled if you-Have an existing portfolio of projects that demonstrate your ability to successfullyapply ML and Data Science methods to real-world business problems.-Have


Azure Arc enabled machine learning (preview) - Azure Machine Learning

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Cluster administrator privileges are needed to create labels for cluster nodes. If this property is specified, training jobs are scheduled to run on nodes with the specified node labels. You can use nodeSelector to target a subset of nodes for training workload placement. This can be useful in scenarios where a cluster has different SKUs, or different types of nodes such as CPU or GPU nodes. For example, you could create node labels for all GPU nodes and define an instanceType for the GPU node pool.


Personalized Machine Learning: Online Supplement

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The book is currently available in draft form as a downloadable pdf. Every day we interact with machine learning systems that personalize their predictions to individual users, whether to recommend movies, find new friends or dating partners, or organize our news feeds. Such systems involve several modalities of data, ranging from sequences of clicks or purchases, to rich modalities involving text, images, or social interactions. While settings and data modalities vary significantly, in this book we introduce a common set of principles and methods that underpin the design of personalized predictive models. The book begins by revising "traditional" machine learning models, with a special focus on how they should be adapted to settings involving user data.


Support Vector Machines in Python

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Please consider watching this video if any section of this article is unclear. How to set up your programming environment can be found at the start of: Episode 4.3 We can now use the support vector machine to classify apples and oranges given the fruit's weight and size. For example -- let's say we recorded a fruit to have a weight of 70 grams and size of 4.6cm. We obtain a prediction of this fruit being an orange. Looking at the graph in the scatterplot above we note the recording of 70 grams and size of 4.6cm lies below the hyperplane, hence an orange is predicted.


UK's ICO warns over 'Big Data' surveillance threat of live facial recognition in public – TechCrunch

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The UK's chief data protection regulator has warned over reckless and inappropriate use of live facial recognition (LFR) in public places. Publishing an opinion today on the use of this biometric surveillance in public -- to set out what is dubbed as the "rules of engagement" -- the information commissioner, Elizabeth Denham, also noted that a number of investigations already undertaken by her office into planned applications of the tech have found problems in all cases. "I am deeply concerned about the potential for live facial recognition (LFR) technology to be used inappropriately, excessively or even recklessly. When sensitive personal data is collected on a mass scale without people's knowledge, choice or control, the impacts could be significant," she warned in a blog post. "Uses we've seen included addressing public safety concerns and creating biometric profiles to target people with personalised advertising. "It is telling that none of the organisations involved in our completed investigations were able to fully justify the processing and, of those systems that went live, none were fully compliant with the requirements of data protection law.


The future of sports is algorithms, not athletes

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Spectators sit stacked one row behind another, all craning their necks to look down at the miniaturized soccer pitch below them. With bated breath, they watch as a tiny player gently bumps the ball up the pitch toward its opponent. The goal in sight, the attacker has two options -- evade its opponent by expertly moving the ball around it, or send a safer pass to a teammate outside the fray. Like any soccer star, the player chooses glory and begins to putter its feet back and forth to move the ball -- when it begins to lose balance. And down it falls, like a felled tree.


Game On! MIT, Allen AI & Microsoft Open-Source a Suite of AI Programming Puzzles

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Programming competition problems are pervasive in the AI community. They can be used to evaluate programmers' abilities to solve artificial tasks as well as to test the limits of state-of-the-art algorithms. A research team from MIT, Allen Institute for AI and Microsoft Research recently introduced Python Programming Puzzles (P3), a novel and open-source collection of programming challenges that capture the essence of puzzles and can be used to teach and evaluate an AI's programming proficiency. The proposed puzzles take the form of a Python function with the answer as an argument. The goal is to find an input x that makes the output of the function true, i.e., a valid answer x satisfies f(x) True.


U.S. push for self-driving cars faces union, lawyers opposition

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The U.S. Senate Commerce Committee on Wednesday again rejected attempts to lift regulations to allow for the deployment of thousands of autonomous vehicles as union groups and attorneys campaign against the legislative proposal. The committee rebuffed the bid by Republican Senator John Thune to attach measures lifting regulations on autonomous vehicles to a $78 billion surface transportation bill after he sought last month to attach it in May to a bill on China tech policy. Thune has proposed granting the U.S. National Highway Traffic Safety Administration (NHTSA) the power to grant exemptions for tens of thousands of self-driving vehicles per manufacturer from safety standards written with human drivers in mind. The surface bill, which would boost funding for Amtrak and other transportation needs, was approved by the committee on a 25-3 vote. Thune and other lawmakers have sought for nearly five years to win approval.