Outbrain customer success - CSM TLV meetup June 2016Gideon Cohen
Low touch CSM challenges and technics: how to engage with customers during the various customer life cycles. Presentation by Miko Levy, VP Customer Acquisition, Outbrain
A presentation by Adi Agasi, Customers Satisfaction Manager at Comverese Ltd, given at the B2B Customer Success & Experience Seminar, June 18 2014 Tel-Aviv
Retention and upsale in using customer dataGideon Cohen
A presentation given by Ariel Autnik, VP Customer Success at Panaya at the B2B Customer Succcess & Experience on June 18 2014 Tel-Aviv. Describing how to turn customer data to actionable knowledge in SaaS products.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Outbrain customer success - CSM TLV meetup June 2016Gideon Cohen
Low touch CSM challenges and technics: how to engage with customers during the various customer life cycles. Presentation by Miko Levy, VP Customer Acquisition, Outbrain
A presentation by Adi Agasi, Customers Satisfaction Manager at Comverese Ltd, given at the B2B Customer Success & Experience Seminar, June 18 2014 Tel-Aviv
Retention and upsale in using customer dataGideon Cohen
A presentation given by Ariel Autnik, VP Customer Success at Panaya at the B2B Customer Succcess & Experience on June 18 2014 Tel-Aviv. Describing how to turn customer data to actionable knowledge in SaaS products.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
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