MS Sql Server: Introduction To Datamining Suing Sql Server
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Data mining involves analyzing large datasets to discover patterns. It can be used to better understand systems by studying trends and patterns in vast amounts of data. Data mining uses classification, clustering, association, and regression algorithms to organize data and discover patterns. The data mining process involves data collection, cleaning, transformation, modeling, and assessment. Examples of data mining applications include customer relationship management, enterprise resource planning, and analyzing web server logs.
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Ad hoc & sensor networks, Adaptive applications, Aeronautical Engineering, Aerospace Engineering
Agricultural Engineering, AI and Image Recognition, Allied engineering materials, Applied mechanics,
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Data mining
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Vaibhav Gupta
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in/vaigup
boundaryml/baml
Imagine if every API call you made
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boundaryml/baml
Imagine if every LLM call you made
failed only 5% of the time
boundaryml/baml
Imagine if every LLM call you made
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boundaryml/baml
Fault tolerant systems are hard
but now everything must be
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boundaryml/baml
We need to change how we
think about these systems
Aaron Villalpando
Cofounder @ Boundary
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boundaryml/baml
We used to write websites like this:
boundaryml/baml
But now we do this:
boundaryml/baml
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boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
boundaryml/baml
Problems web dev had:
Strings. Strings everywhere.
State management was impossible.
Dynamic components? forget about it.
Reuse components? Good luck.
Iteration loops took minutes.
Low engineering rigor
boundaryml/baml
React added engineering rigor
boundaryml/baml
The syntax we use changes how we
think about problems
boundaryml/baml
We used to write agents like this:
boundaryml/baml
Problems agents have:
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
boundaryml/baml
Problems agents have:
Strings. Strings everywhere.
Context management is impossible.
Changing one thing breaks another.
New models come out all the time.
Iteration loops take minutes.
Low engineering rigor
boundaryml/baml
Agents need
the expressiveness of English,
but the structure of code
F*** You, Show Me The Prompt.
boundaryml/baml
<show don’t tell>
Less prompting +
More engineering
=
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Maintainability
BAML
Sam
Greg Antonio
Chris
turned down
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of the earliest
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UiPath Automation Suite – Cas d'usage d'une NGO internationale basée à Genève
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Cette séance sera consacrée à un retour d'expérience de la part d'une organisation non gouvernementale basée à Genève. L'équipe en charge de la plateforme UiPath pour cette NGO nous présentera la variété des automatisations mis en oeuvre au fil des années : de la gestion des donations au support des équipes sur les terrains d'opération.
Au délà des cas d'usage, cette session sera aussi l'opportunité de découvrir comment cette organisation a déployé UiPath Automation Suite et Document Understanding.
Cette session a été diffusée en direct le 7 mai 2025 à 13h00 (CET).
Découvrez toutes nos sessions passées et à venir de la communauté UiPath à l’adresse suivante : https://community.uipath.com/geneva/.
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Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian Efficiency is essential to support responsiveness w.r.t. ever-growing datasets, especially for Deep Learning (DL) systems. DL frameworks have traditionally embraced deferred execution-style DL code—supporting symbolic, graph-based Deep Neural Network (DNN) computation. While scalable, such development is error-prone, non-intuitive, and difficult to debug. Consequently, more natural, imperative DL frameworks encouraging eager execution have emerged but at the expense of run-time performance. Though hybrid approaches aim for the “best of both worlds,” using them effectively requires subtle considerations to make code amenable to safe, accurate, and efficient graph execution—avoiding performance bottlenecks and semantically inequivalent results. We discuss the engineering aspects of a refactoring tool that automatically determines when it is safe and potentially advantageous to migrate imperative DL code to graph execution and vice-versa.
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...
Hybridize Functions: A Tool for Automatically Refactoring Imperative Deep Lea...Raffi Khatchadourian
MS Sql Server: Introduction To Datamining Suing Sql Server
- 1. 14SQL SERVER: INTRODUCTION TO DATA MINING USING SQL SERVER
- 2. What is a Data Mining?Data mining is the process of analyzing a data set to find patternsData mining can also defined as deriving of knowledge from raw-data
- 3. AliasesData mining is also known by the following terms:
- 4. Importance of Data miningThe Amount of data in the contemporary world is humungous. By studying this data and understanding the trend and patterns, one can understand the system better. Due to data mining, conclusions which are profitable for an organization or decisions which may help a librarian manage books better: may be arrived at. Pervasiveness of data:CRM(Customer Relationship Management)ERP(Enterprise Resource Planning)Database serversData PoolWeb Server Logs
- 5. Data MiningThe traditional SQL queries that we learnt till now follow the method of ‘querying’ and based upon the response, ‘explore’ the system more. Query and Exploration MethodData Mining MethodThe Data mining methodology hence takes the opposite direction as that of query methodsHere, the important attribute on which the analysis is based is the ‘name’. Hence, it is called as the class
- 6. ApplicationsThe Application of data mining covers a wide domain. Any place where data is involved can be operated upon using data mining. Some of the real world applications of data mining are as follows:
- 7. Algorithms for Data miningThe Data mining systems utilize a wide variety of algorithms. The Four common algorithm types are:
- 8. Tasks involved in Data MiningThe Process of data mining is divided into various steps as follows: Classification
- 9. Clustering
- 10. Association
- 11. Regression
- 12. ForecastingLet us have a look at them
- 13. ClassificationClassification is the process of grouping items into meaningful groups. The Groups are later treated as a single element and the relation between the groups are analyzed. Simply put, it is the task of assigning a group to each case.Example:Data Set
- 14. ClusteringClustering is the process of grouping data items based on some attributesExample:Data SetClustered based on nearness
- 15. Data mining algorithmsData Mining is a complex methodology which needs advanced algorithms operating on useful data.The Data mining algorithms are mainly divided into 2 types:Supervising algorithmUnsupervising algorithmIn a supervising algorithm, the system needs a target(may be a set of attributes) to learn againstWhereas the Unsupervising algorithm, iterates till the boundaries of the problem are reached
- 16. Regression and ForecastingREGRESSION:In some problems, the analysis, instead of looking for patterns that describe prime attributes (classes), we look for patterns in numerical valuesThere are 2 types of regression: 1.Linear regression 2. Logostic RegressionRegression is used to solve many business problems like predicting sea-wave patterns, temperature, air pressure, and humidity.FORECASTING:As the name suggests, it is the fore telling of data from that which currently exists.Eg: Election results forecast
- 17. Steps to takeThe Process of data mining consists of various steps which are listed below:Data Collection: Collect dataData Cleaning: Eliminate unwanted, irrelevant and wrong dataData Transformation: Change data into a word that can be used for data mining. The Types of data transformations are:Numerical TransformationGroupingAggregation: Form groups of minute data items and handle them as aggregates. It makes the process much easier.Missing Value handling: Predict missing values or eliminate all such valuesRemoving Outliers: Remove invalid dataModel Building: Build the data mining model.Model Assessment Test with a large amount of data. If a model needs change, make it immediately.
- 18. What to do next?The Microsoft Office 2007 supports a wide variety of data mining tools. Visit the site www.sqlserverdatamining.com and download the MS Access 2007 Add-on for data mining. Install the add-on.Working with the Access 07 Data mining tools will be handled in the next set of presentations.Summary Data mining
- 19. Applications
- 20. Classification
- 21. Clustering
- 22. Algorithms
- 23. Regressions
- 24. Steps involvedVisit more self help tutorialsPick a tutorial of your choice and browse through it at your own pace.The tutorials section is free, self-guiding and will not involve any additional support.Visit us at www.dataminingtools.net