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Articles related to "based"


E.ventures opens up new office in Paris

  • VC firm e.ventures is expanding its footprint in Europe with a new office in Paris, as well as a new Paris-based partner.
  • Jonathan Userovici, who previously worked for Idinvest Partners, is joining e.ventures as partner and head of the Paris office.
  • Last year, the firm raised two new funds — the first was a $225 million U.S.-focused fund and the second was a $175 million fund based in Berlin and focused on Europe.
  • The Paris team will deploy some capital in French startups with a sweet spot between €1 million and €10 million.
  • As for Jonathan Userovici, after five years at Idinvest Partners, he has been involved with some promising French startups.
  • Thanks to e.ventures’ distributed team, the VC firm hopes it can spot good investment opportunities in Europe and help them scale globally.

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AWS ALBs have finally gRPC Support

  • Application Load Balancer (ALB) now supports gRPC protocol.
  • With this release, you can use ALB to route and load balance your gRPC traffic between microservices or between gRPC enabled clients and services.
  • ALB provides rich content based routing features that will let you inspect gRPC calls and route them to the appropriate target group based on the service and method requested.
  • Within a target group, ALB will use gRPC specific health checks to determine availability of targets and provide gRPC specific access logs to monitor your traffic.
  • This release also provides customers the ability to configure HTTP/2 as the protocol for your target groups.
  • Doing so will enable an end-to-end HTTP/2 flow from clients to targets, providing you the benefits of HTTP/2 optimizations even when not using gRPC.
  • The support for gRPC and end-to-end HTTP/2 is available for existing and new Application Load Balancers at no extra charge in all AWS Regions.

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Mailchimp outlines right to ban users for “inaccurate” content

  • There is strong and lingering suspicion, especially among those continuously targeted with such censorship on social media but also on other online services, that “misinformation” is often simply something these companies don’t like for political or ideological reasons – and labeling content in that way makes it easy to get rid of, but also justify that action as not simply blatant suppression of speech.
  • At least Mailchimp, a US email and marketing automation service – doesn’t even try to pretend there is some objective, consensus-based way in determining what’s false and what’s true.
  • Mailchimp is a popular service for email campaigns, but it is not so big that it’s irreplaceable and beside lamenting the current state of affairs with freedom of expression on the internet, some customers are recommending switching to other services who do not restrict their customers’ behavior and business in this way.

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Bird’s-Eye View of Reinforcement Learning Algorithms Taxonomy

  • Without wasting any more time, let’s take a deep breath, make a cup of chocolate, and I invite you to learn with me about the bird’s-eye view of the RL algorithms taxonomy!
  • Another way to classify RL algorithms is by considering what component is optimized by the algorithm — the value function or the policy.
  • Before we deep diver, let’s learn about policy and value function first.
  • Value function is a function that measures how good a state is based on the prediction of future reward or known as a return.
  • We can say that algorithms classified as on-policy are “learning on the job.” In other words, the algorithm attempts to learn about policy π from experience sampled from π.
  • After reading this article, you should have known about how the RL algorithms are classified based on several point-of-views.
  • We will learn more about the value-based and policy-based algorithms in the future episodes.

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2021 Ten Exaflop European Supercomputer in 2021

  • The European supercomputer Leonardo, managed by Cineca, that will be installed at the end of 2021 in the new data center located in Bologna, will be based on Atos BullSequana XH2000 technology, and feature nearly 14,000 NVIDIA Ampere architecture-based GPUs and NVIDIA Mellanox HDR InfiniBand.
  • It will be based on Atos BullSequana XH2000 technology and use 14,000 next generation NVIDIA Ampere architecture-based GPUs and NVIDIA Mellanox HDR InfiniBand.
  • It will be an Apollo 6500-based system with 560 A100 graphics cards to deliver nearly 350 petaflops of performance for academic and industrial simulations, data analytics, and AI.
  • It will also use NVIDIA Mellanox HDR 200Gb/s InfiniBand connectivity, with smart in-network computing acceleration engines that enable extremely low latency and high data throughput to provide the highest AI and HPC application performance and scalability.
  • Leonardo will feature nearly 14 000 NVIDIA Ampere architecture-based GPUs. It will deliver 10 exaflops of FP16 AI performance.

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Movie Recommender System

  • The ratings make up the explicit responses from the users, which will be used for building collaborative-based filtering systems subsequently.
  • From the ratings of movies A and B, based on the cosine similarity, Maria is more similar to Sally than Kim is to Sally.
  • It shows the ratings of three movies A, B and C given by users Maria and Kim. The k-NN model tries to predict Sally’s rating for movie C (not rated yet) when Sally has already rated movies A and B.
  • Both the users and movies are embedded into 50-dimensional (n = 50) array vectors for use in the training and test data.
  • From the training and validation loss graph, it shows that the neural-based model has a good fit.
  • The image above shows the movies that user 838 has rated highly in the past and what the neural-based model recommends.

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Movie Recommender System

  • The ratings make up the explicit responses from the users, which will be used for building collaborative-based filtering systems subsequently.
  • From the ratings of movies A and B, based on the cosine similarity, Maria is more similar to Sally than Kim is to Sally.
  • It shows the ratings of three movies A, B and C given by users Maria and Kim. The k-NN model tries to predict Sally’s rating for movie C (not rated yet) when Sally has already rated movies A and B.
  • Both the users and movies are embedded into 50-dimensional (n = 50) array vectors for use in the training and test data.
  • From the training and validation loss graph, it shows that the neural-based model has a good fit.
  • The image above shows the movies that user 838 has rated highly in the past and what the neural-based model recommends.

save | comments | report | share on


Movie Recommender System

  • The ratings make up the explicit responses from the users, which will be used for building collaborative-based filtering systems subsequently.
  • From the ratings of movies A and B, based on the cosine similarity, Maria is more similar to Sally than Kim is to Sally.
  • It shows the ratings of three movies A, B and C given by users Maria and Kim. The k-NN model tries to predict Sally’s rating for movie C (not rated yet) when Sally has already rated movies A and B.
  • Both the users and movies are embedded into 50-dimensional (n = 50) array vectors for use in the training and test data.
  • From the training and validation loss graph, it shows that the neural-based model has a good fit.
  • The image above shows the movies that user 838 has rated highly in the past and what the neural-based model recommends.

save | comments | report | share on


Movie Recommender System

  • The ratings make up the explicit responses from the users, which will be used for building collaborative-based filtering systems subsequently.
  • From the ratings of movies A and B, based on the cosine similarity, Maria is more similar to Sally than Kim is to Sally.
  • It shows the ratings of three movies A, B and C given by users Maria and Kim. The k-NN model tries to predict Sally’s rating for movie C (not rated yet) when Sally has already rated movies A and B.
  • Both the users and movies are embedded into 50-dimensional (n = 50) array vectors for use in the training and test data.
  • From the training and validation loss graph, it shows that the neural-based model has a good fit.
  • The image above shows the movies that user 838 has rated highly in the past and what the neural-based model recommends.

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US touts largest ever seizure of Iranian oil and weapons

  • Washington (CNN) - The US announced Thursday that it has sold petroleum from its largest-ever seizure of Iranian fuel for more than $40 million and will direct most of the proceeds to a fund for the victims of state-sponsored terrorism.
  • Officials from the State Department and Department of Justice also said that the US had conducted its largest ever seizure of Iranian weapons in late 2019 and early 2020.
  • The news of the seizures and sale came shortly after the US Treasury Department unveiled sanctions on eight entities based in Iran, China and Singapore for selling and purchasing Iranian petrochemical products.
  • Officials said Thursday that they were making the fuel and weapons announcements because the US District Court for the District of Columbia had just unsealed the orders allowing for the sale and seizure.

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