�ݺ�ߣshows by User: gakhov

�ݺ�ߣshows by User: gakhov / http://www.slideshare.net/images/logo.gif �ݺ�ߣshows by User: gakhov / Fri, 29 May 2020 15:51:08 GMT �ݺ�ߣShare feed for �ݺ�ߣshows by User: gakhov Let's start GraphQL: structure, behavior, and architecture /slideshow/lets-start-graphql-structure-behavior-and-architecture-234729286/234729286 ferret-graphql-talk-2-200529155108
In this talk, I describe the path to start with GraphQL in a company that has experience with Python stack and REST API. We go from the definition of GraphQL, via behavioral aspects and data management, to the most common architectural questions.]]>
In this talk, I describe the path to start with GraphQL in a company that has experience with Python stack and REST API. We go from the definition of GraphQL, via behavioral aspects and data management, to the most common architectural questions.]]> Fri, 29 May 2020 15:51:08 GMT /slideshow/lets-start-graphql-structure-behavior-and-architecture-234729286/234729286 gakhov@slideshare.net(gakhov) Let's start GraphQL: structure, behavior, and architecture gakhov In this talk, I describe the path to start with GraphQL in a company that has experience with Python stack and REST API. We go from the definition of GraphQL, via behavioral aspects and data management, to the most common architectural questions. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ferret-graphql-talk-2-200529155108-thumbnail.jpg?width=120&height=120&fit=bounds" /> In this talk, I describe the path to start with GraphQL in a company that has experience with Python stack and REST API. We go from the definition of GraphQL, via behavioral aspects and data management, to the most common architectural questions.

Let's start GraphQL: structure, behavior, and architecture from Andrii Gakhov

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0 Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications /slideshow/exceeding-classical-probabilistic-data-structures-in-data-intensive-applications/169239364 euroscipy-2019-gakhov-v20190905-190905082753
We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications.]]>
We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications.]]> Thu, 05 Sep 2019 08:27:53 GMT /slideshow/exceeding-classical-probabilistic-data-structures-in-data-intensive-applications/169239364 gakhov@slideshare.net(gakhov) Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications gakhov We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/euroscipy-2019-gakhov-v20190905-190905082753-thumbnail.jpg?width=120&height=120&fit=bounds" /> We interact with an increasing amount of data but classical data structures and algorithms can't fit our requirements anymore. This talk is to present the probabilistic algorithms and data structures and describe the main areas of their applications.

Exceeding Classical: Probabilistic Data Structures in Data Intensive Applications from Andrii Gakhov

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0 Too Much Data? - Just Sample, Just Hash, ... /slideshow/too-much-data-just-sample-just-hash/147934684 cs-pittsburgh-meetup-190528110359
Code & Supply | Pittsburgh Meetup | May 31, 2019 Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic. Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com]]>
Code & Supply | Pittsburgh Meetup | May 31, 2019 Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic. Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com]]> Tue, 28 May 2019 11:03:59 GMT /slideshow/too-much-data-just-sample-just-hash/147934684 gakhov@slideshare.net(gakhov) Too Much Data? - Just Sample, Just Hash, ... gakhov Code & Supply | Pittsburgh Meetup | May 31, 2019 Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic. Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/cs-pittsburgh-meetup-190528110359-thumbnail.jpg?width=120&height=120&fit=bounds" /> Code & Supply | Pittsburgh Meetup | May 31, 2019 Probabilistic Data Structures and Algorithms (PDSA) is a common name of data structures based on different hashing techniques. They have been incorporated into Spark SQL. They are also used by Amazon Redshift and Google BigQuery, Redis and Elasticsearch, and many others. Consequently, PDSA is not just some interesting academic topic. Book "Probabilistic Data Structures and Algorithms for Big Data Applications" (ISBN: 978-3748190486 ) https://pdsa.gakhov.com

Too Much Data? - Just Sample, Just Hash, ... from Andrii Gakhov

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0 DNS Delegation /gakhov/dns-delegation-87991289 dns-delegation-180214151843
A Lightning talk about load distribution using DNS Delegation]]>
A Lightning talk about load distribution using DNS Delegation]]> Wed, 14 Feb 2018 15:18:43 GMT /gakhov/dns-delegation-87991289 gakhov@slideshare.net(gakhov) DNS Delegation gakhov A Lightning talk about load distribution using DNS Delegation <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/dns-delegation-180214151843-thumbnail.jpg?width=120&height=120&fit=bounds" /> A Lightning talk about load distribution using DNS Delegation

DNS Delegation from Andrii Gakhov

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0 Implementing a Fileserver with Nginx and Lua /gakhov/implementing-a-fileserver-with-nginx-and-lua implementingafileserverwithnginxandlua-170301063651
Using the power of Nginx it is easy to implement quite complex logic of file upload with metadata and authorization support, and without need of any heavy application server. In this article you can find the basic implementation of such Fileserver using Nginx and Lua only.]]>
Using the power of Nginx it is easy to implement quite complex logic of file upload with metadata and authorization support, and without need of any heavy application server. In this article you can find the basic implementation of such Fileserver using Nginx and Lua only.]]> Wed, 01 Mar 2017 06:36:51 GMT /gakhov/implementing-a-fileserver-with-nginx-and-lua gakhov@slideshare.net(gakhov) Implementing a Fileserver with Nginx and Lua gakhov Using the power of Nginx it is easy to implement quite complex logic of file upload with metadata and authorization support, and without need of any heavy application server. In this article you can find the basic implementation of such Fileserver using Nginx and Lua only. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/implementingafileserverwithnginxandlua-170301063651-thumbnail.jpg?width=120&height=120&fit=bounds" /> Using the power of Nginx it is easy to implement quite complex logic of file upload with metadata and authorization support, and without need of any heavy application server. In this article you can find the basic implementation of such Fileserver using Nginx and Lua only.

Implementing a Fileserver with Nginx and Lua from Andrii Gakhov

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0 Pecha Kucha: Ukrainian Food Traditions /slideshow/pecha-kucha-ukrainian-food-traditions/70224711 pechakucha-1-2016-161217091413
Have you heard about Salad Olivje, Vereniki, Pirogi and Bliny, but you are unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :)]]>
Have you heard about Salad Olivje, Vereniki, Pirogi and Bliny, but you are unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :)]]> Sat, 17 Dec 2016 09:14:13 GMT /slideshow/pecha-kucha-ukrainian-food-traditions/70224711 gakhov@slideshare.net(gakhov) Pecha Kucha: Ukrainian Food Traditions gakhov Have you heard about Salad Olivje, Vereniki, Pirogi and Bliny, but you are unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :) <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pechakucha-1-2016-161217091413-thumbnail.jpg?width=120&height=120&fit=bounds" /> Have you heard about Salad Olivje, Vereniki, Pirogi and Bliny, but you are unsure what it is all about? This easy Pecha Kucha presentation can help you to become an expert :)

Pecha Kucha: Ukrainian Food Traditions from Andrii Gakhov

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0 Probabilistic data structures. Part 4. Similarity /slideshow/probabilistic-data-structures-part-4-similarity/69774022 probabilitydatastructurespart4-161202203733
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In this presentation, I described popular algorithms that employed Locality Sensitive Hashing (LSH) to solve similarity-related problems. I started with LSH in general and then switched to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach came with some math that was behind it and simple examples to clarify the theory statements.]]>
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In this presentation, I described popular algorithms that employed Locality Sensitive Hashing (LSH) to solve similarity-related problems. I started with LSH in general and then switched to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach came with some math that was behind it and simple examples to clarify the theory statements.]]> Fri, 02 Dec 2016 20:37:33 GMT /slideshow/probabilistic-data-structures-part-4-similarity/69774022 gakhov@slideshare.net(gakhov) Probabilistic data structures. Part 4. Similarity gakhov The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In this presentation, I described popular algorithms that employed Locality Sensitive Hashing (LSH) to solve similarity-related problems. I started with LSH in general and then switched to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach came with some math that was behind it and simple examples to clarify the theory statements. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/probabilitydatastructurespart4-161202203733-thumbnail.jpg?width=120&height=120&fit=bounds" /> The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In this presentation, I described popular algorithms that employed Locality Sensitive Hashing (LSH) to solve similarity-related problems. I started with LSH in general and then switched to such algorithms as MinHash (LSH for Jaccard similarity) and SimHash (LSH for cosine similarity). Each approach came with some math that was behind it and simple examples to clarify the theory statements.

Probabilistic data structures. Part 4. Similarity from Andrii Gakhov

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0 Probabilistic data structures. Part 3. Frequency /slideshow/probabilistic-data-structures-part-3-frequency/66330104 probabilisticdatastructurespart3-160923061849
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.]]>
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.]]> Fri, 23 Sep 2016 06:18:49 GMT /slideshow/probabilistic-data-structures-part-3-frequency/66330104 gakhov@slideshare.net(gakhov) Probabilistic data structures. Part 3. Frequency gakhov The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/probabilisticdatastructurespart3-160923061849-thumbnail.jpg?width=120&height=120&fit=bounds" /> The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described popular and very simple data structures and algorithms to estimate the frequency of elements or find most occurred values in a data stream, such as Count-Min Sketch, Majority Algorithm, and Misra-Gries Algorithm. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.

Probabilistic data structures. Part 3. Frequency from Andrii Gakhov

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0 Probabilistic data structures. Part 2. Cardinality /slideshow/probabilistic-data-structures-part-2-cardinality/64409613 probabilisticdatastructurespart2-160726192154
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.]]>
The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.]]> Tue, 26 Jul 2016 19:21:54 GMT /slideshow/probabilistic-data-structures-part-2-cardinality/64409613 gakhov@slideshare.net(gakhov) Probabilistic data structures. Part 2. Cardinality gakhov The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/probabilisticdatastructurespart2-160726192154-thumbnail.jpg?width=120&height=120&fit=bounds" /> The book "Probabilistic Data Structures and Algorithms in Big Data Applications" is now available at Amazon and from local bookstores. More details at https://pdsa.gakhov.com In the presentation, I described common data structures and algorithms to estimate the number of distinct elements in a set (cardinality), such as Linear Counting, HyperLogLog, and HyperLogLog++. Each approach comes with some math that is behind it and simple examples to clarify the theory statements.

Probabilistic data structures. Part 2. Cardinality from Andrii Gakhov

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0 Вероятностные структуры данных /slideshow/ss-61915790/61915790 probabilitydatastructures-rus-160511172355
Рассмотрены примеры простейших вероятностных структур данных: Bloom Filter, Count Min Sketch, Linear Counting, MinHash]]>
Рассмотрены примеры простейших вероятностных структур данных: Bloom Filter, Count Min Sketch, Linear Counting, MinHash]]> Wed, 11 May 2016 17:23:55 GMT /slideshow/ss-61915790/61915790 gakhov@slideshare.net(gakhov) Вероятностные структуры данных gakhov Рассмотрены примеры простейших вероятностных структур данных: Bloom Filter, Count Min Sketch, Linear Counting, MinHash <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/probabilitydatastructures-rus-160511172355-thumbnail.jpg?width=120&height=120&fit=bounds" /> Рассмотрены примеры простейших вероятностных структур данных: Bloom Filter, Count Min Sketch, Linear Counting, MinHash

Вероятностные структуры данных from Andrii Gakhov

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0 Recurrent Neural Networks. Part 1: Theory /gakhov/recurrent-neural-networks-part-1-theory ferret-rnn-151211092908
In presentation I cover basic aspects of the popular RNN architectures: LSTM and GRU.]]>
In presentation I cover basic aspects of the popular RNN architectures: LSTM and GRU.]]> Fri, 11 Dec 2015 09:29:07 GMT /gakhov/recurrent-neural-networks-part-1-theory gakhov@slideshare.net(gakhov) Recurrent Neural Networks. Part 1: Theory gakhov In presentation I cover basic aspects of the popular RNN architectures: LSTM and GRU. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/ferret-rnn-151211092908-thumbnail.jpg?width=120&height=120&fit=bounds" /> In presentation I cover basic aspects of the popular RNN architectures: LSTM and GRU.

Recurrent Neural Networks. Part 1: Theory from Andrii Gakhov

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0 Apache Big Data Europe 2015: Selected Talks /slideshow/apache-big-data-europe-2015-selected-talks/53986717 apache-bigdata-ferret-151015174649-lva1-app6891
Selected talks from Apache Big Data Europe 2015]]>
Selected talks from Apache Big Data Europe 2015]]> Thu, 15 Oct 2015 17:46:49 GMT /slideshow/apache-big-data-europe-2015-selected-talks/53986717 gakhov@slideshare.net(gakhov) Apache Big Data Europe 2015: Selected Talks gakhov Selected talks from Apache Big Data Europe 2015 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/apache-bigdata-ferret-151015174649-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> Selected talks from Apache Big Data Europe 2015

Apache Big Data Europe 2015: Selected Talks from Andrii Gakhov

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0 Swagger / Quick Start Guide /slideshow/swagger-quick-start-guide/50588305 swagger-150716093219-lva1-app6891
This presentation shows main aspects on using Swagger for API developers]]>
This presentation shows main aspects on using Swagger for API developers]]> Thu, 16 Jul 2015 09:32:19 GMT /slideshow/swagger-quick-start-guide/50588305 gakhov@slideshare.net(gakhov) Swagger / Quick Start Guide gakhov This presentation shows main aspects on using Swagger for API developers <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/swagger-150716093219-lva1-app6891-thumbnail.jpg?width=120&height=120&fit=bounds" /> This presentation shows main aspects on using Swagger for API developers

Swagger / Quick Start Guide from Andrii Gakhov

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0 API Days Berlin highlights /slideshow/api-days-berlin-47581500/47581500 api-days-ferret-150429164337-conversion-gate01
Some highlights and impression from the API Days Berlin + API Strat Europe // April 24-25, 2015 * microservices * hypermedia API * swagger * 3scale API Gateway]]>
Some highlights and impression from the API Days Berlin + API Strat Europe // April 24-25, 2015 * microservices * hypermedia API * swagger * 3scale API Gateway]]> Wed, 29 Apr 2015 16:43:37 GMT /slideshow/api-days-berlin-47581500/47581500 gakhov@slideshare.net(gakhov) API Days Berlin highlights gakhov Some highlights and impression from the API Days Berlin + API Strat Europe // April 24-25, 2015 * microservices * hypermedia API * swagger * 3scale API Gateway <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/api-days-ferret-150429164337-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Some highlights and impression from the API Days Berlin + API Strat Europe // April 24-25, 2015 * microservices * hypermedia API * swagger * 3scale API Gateway

API Days Berlin highlights from Andrii Gakhov

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0 ELK - What's new and showcases /gakhov/elk-46146389 elk-ferret-2015-150322165342-conversion-gate01
Overview of ELK current state Elasticsearch new aggregations and examples how to easy use them to solve some interesting problems]]>
Overview of ELK current state Elasticsearch new aggregations and examples how to easy use them to solve some interesting problems]]> Sun, 22 Mar 2015 16:53:42 GMT /gakhov/elk-46146389 gakhov@slideshare.net(gakhov) ELK - What's new and showcases gakhov Overview of ELK current state Elasticsearch new aggregations and examples how to easy use them to solve some interesting problems <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/elk-ferret-2015-150322165342-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Overview of ELK current state Elasticsearch new aggregations and examples how to easy use them to solve some interesting problems

ELK - What's new and showcases from Andrii Gakhov

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0 Apache Spark Overview @ ferret /slideshow/apache-spark-overview-ferret/42774774 spark-overview-ferret-141216165437-conversion-gate01
Overview of Apache Spark and its top components]]>
Overview of Apache Spark and its top components]]> Tue, 16 Dec 2014 16:54:37 GMT /slideshow/apache-spark-overview-ferret/42774774 gakhov@slideshare.net(gakhov) Apache Spark Overview @ ferret gakhov Overview of Apache Spark and its top components <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/spark-overview-ferret-141216165437-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Overview of Apache Spark and its top components

Apache Spark Overview @ ferret from Andrii Gakhov

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0 Data Mining - lecture 8 - 2014 /slideshow/da-42067508/42067508 8-141126160714-conversion-gate01
Data Mining - lecture 8 - 2014]]>
Data Mining - lecture 8 - 2014]]> Wed, 26 Nov 2014 16:07:14 GMT /slideshow/da-42067508/42067508 gakhov@slideshare.net(gakhov) Data Mining - lecture 8 - 2014 gakhov Data Mining - lecture 8 - 2014 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/8-141126160714-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data Mining - lecture 8 - 2014

Data Mining - lecture 8 - 2014 from Andrii Gakhov

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0 Data Mining - lecture 7 - 2014 /slideshow/7-41296721/41296721 7-141108112721-conversion-gate02
Data Mining - lecture 7 - 2014]]>
Data Mining - lecture 7 - 2014]]> Sat, 08 Nov 2014 11:27:21 GMT /slideshow/7-41296721/41296721 gakhov@slideshare.net(gakhov) Data Mining - lecture 7 - 2014 gakhov Data Mining - lecture 7 - 2014 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/7-141108112721-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data Mining - lecture 7 - 2014

Data Mining - lecture 7 - 2014 from Andrii Gakhov

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0 Data Mining - lecture 6 - 2014 /gakhov/data-mining-lecture-6-2014 6-141026104514-conversion-gate01
Data Mining - lecture 6 - 2014 Lecture1 - /gakhov/data-mining-lecture-1-2014 Lecture2 - /gakhov/data-mining-lecture-2-2014 Lecture3 - /gakhov/3-39206548 Lecture4 - /gakhov/4-39539775 Lecture5 - /gakhov/5-40343377 Lecture6 - /gakhov/data-mining-lecture-6-2014 Lecture7 - /gakhov/7-41296721 Lecture8 - /gakhov/da-42067508]]>
Data Mining - lecture 6 - 2014 Lecture1 - /gakhov/data-mining-lecture-1-2014 Lecture2 - /gakhov/data-mining-lecture-2-2014 Lecture3 - /gakhov/3-39206548 Lecture4 - /gakhov/4-39539775 Lecture5 - /gakhov/5-40343377 Lecture6 - /gakhov/data-mining-lecture-6-2014 Lecture7 - /gakhov/7-41296721 Lecture8 - /gakhov/da-42067508]]> Sun, 26 Oct 2014 10:45:14 GMT /gakhov/data-mining-lecture-6-2014 gakhov@slideshare.net(gakhov) Data Mining - lecture 6 - 2014 gakhov Data Mining - lecture 6 - 2014 Lecture1 - /gakhov/data-mining-lecture-1-2014 Lecture2 - /gakhov/data-mining-lecture-2-2014 Lecture3 - /gakhov/3-39206548 Lecture4 - /gakhov/4-39539775 Lecture5 - /gakhov/5-40343377 Lecture6 - /gakhov/data-mining-lecture-6-2014 Lecture7 - /gakhov/7-41296721 Lecture8 - /gakhov/da-42067508 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/6-141026104514-conversion-gate01-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data Mining - lecture 6 - 2014 Lecture1 - /gakhov/data-mining-lecture-1-2014 Lecture2 - /gakhov/data-mining-lecture-2-2014 Lecture3 - /gakhov/3-39206548 Lecture4 - /gakhov/4-39539775 Lecture5 - /gakhov/5-40343377 Lecture6 - /gakhov/data-mining-lecture-6-2014 Lecture7 - /gakhov/7-41296721 Lecture8 - /gakhov/da-42067508

Data Mining - lecture 6 - 2014 from Andrii Gakhov

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0 Data Mining - lecture 5 - 2014 /slideshow/5-40343377/40343377 5-141016052215-conversion-gate02
Data Mining - lecture 5]]>
Data Mining - lecture 5]]> Thu, 16 Oct 2014 05:22:15 GMT /slideshow/5-40343377/40343377 gakhov@slideshare.net(gakhov) Data Mining - lecture 5 - 2014 gakhov Data Mining - lecture 5 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/5-141016052215-conversion-gate02-thumbnail.jpg?width=120&height=120&fit=bounds" /> Data Mining - lecture 5

Data Mining - lecture 5 - 2014 from Andrii Gakhov

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0 https://cdn.slidesharecdn.com/profile-photo-gakhov-48x48.jpg?cb=1696835521 Born in Ukraine, studied applied mathematics, defended my Ph.D. in Mathematical Modeling. I have years of IT experience and years of teaching experience at the university. My primary field of interest as a developer is a web programming, with a focus on back-end services and data analysis. Currently, I'm interested in the applications of machine learning and statistical modeling. My research interests are mathematical modeling of physical phenomena, numerical methods for solving integral, differential and pseudo-differential equations, software design and computer experiments. www.gakhov.com https://cdn.slidesharecdn.com/ss_thumbnails/ferret-graphql-talk-2-200529155108-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/lets-start-graphql-structure-behavior-and-architecture-234729286/234729286 Let's start GraphQL: s... https://cdn.slidesharecdn.com/ss_thumbnails/euroscipy-2019-gakhov-v20190905-190905082753-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/exceeding-classical-probabilistic-data-structures-in-data-intensive-applications/169239364 Exceeding Classical: P... https://cdn.slidesharecdn.com/ss_thumbnails/cs-pittsburgh-meetup-190528110359-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/too-much-data-just-sample-just-hash/147934684 Too Much Data? - Just ...