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Ольга Литвиненко
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Ольга Литвиненко
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Ольга Литвиненко
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Ольга Литвиненко
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огляд 4 кл
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How i wrote my own bdd framework
How i wrote my own bdd framework
Mark Maglana
In my experience, the effort and ingenuity a product team invests in the no search results page is indicative of its overall dedication to customer success.Ignoring this special kind of search results page virtually guarantees a mediocre search experience and contributes to obscurity in the ecommerce marketplace. On the other hand, if a team thinks creatively about the case when there are no search results and focuses on customer needs, it can turn a temporary snag in communication into an opportunity for deeper connection and a source of tremendous competitive advantage.
Starting From Zero - Winning Strategies for Zero Results Page
Starting From Zero - Winning Strategies for Zero Results Page
Ramzi Alqrainy
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тести 1
тести 1
Ольга Литвиненко
план вивчення теми
план
план
Ольга Литвиненко
план вивчення теми
план
план
Ольга Литвиненко
досвід вчителя
досвід жосан
досвід жосан
Ольга Литвиненко
Огляд програмного забезпечення
огляд 4 кл
огляд 4 кл
Ольга Литвиненко
How i wrote my own bdd framework
How i wrote my own bdd framework
Mark Maglana
In my experience, the effort and ingenuity a product team invests in the no search results page is indicative of its overall dedication to customer success.Ignoring this special kind of search results page virtually guarantees a mediocre search experience and contributes to obscurity in the ecommerce marketplace. On the other hand, if a team thinks creatively about the case when there are no search results and focuses on customer needs, it can turn a temporary snag in communication into an opportunity for deeper connection and a source of tremendous competitive advantage.
Starting From Zero - Winning Strategies for Zero Results Page
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Ramzi Alqrainy
STACK
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Most large-scale commercial and social websites recommend options, such as products or people to connect with, to users. Recommendation engines sort through massive amounts of data to identify potential user preferences. This presentation, the first in a two-part series, explains the ideas behind recommendation systems and introduces you to the algorithms that power them. In Part 2, learn about some open source recommendation engines you can put to work.
Recommender Systems, Part 1 - Introduction to approaches and algorithms
Recommender Systems, Part 1 - Introduction to approaches and algorithms
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презент.вчителя
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Most large-scale commercial and social websites recommend options, such as products or people to connect with, to users. Recommendation engines sort through massive amounts of data to identify potential user preferences. This presentation, the first in a two-part series, explains the ideas behind recommendation systems and introduces you to the algorithms that power them. In Part 2, learn about some open source recommendation engines you can put to work.
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Overview of the architecture, and benefits of Dell HPC Storage with Intel EE Lustre in High Performance Computing and Big Science workloads. Presented by Andrew Underwood at the Melbourne Big Data User Group - January 2016. Lustre is a trademark of Seagate Technology.
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свято і не тільки Підготували студенти ІІІ курсу Педагогічного коледжу
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