In this session we will go over the fundamentals of functional programming and see how functional programming can help make our code more reusable, stable, scalable and fun.
A talk from CppEurope 2019 about functional programming in C++.
It talks about lambdas, immutability, operations with functions (partial application, currying, functional composition), shows an example and ends with a procedure for refactoring legacy code.
Functional programming uses pure functions that have no side effects to make code more predictable, stable, and easier to test and maintain. It favors immutability over mutable state to avoid side effects. Complex programs are built by composing simple functions together rather than using complex objects. Higher order functions like map, filter, and reduce help avoid loops. Currying allows functions to take parameters one at a time for simpler composition.
INTRODUCTION
COMPARISON BETWEEN NORMAL FUNCTION AND INLINE FUNCTION
PROS AND CONS
WHY WHEN AND HOW TO USED?
GENERAL STRUCTURE OF INLINE FUNCTION
EXAMPLE WITH PROGRAM CODE
The document discusses different types of functions in C++ including:
1) Main functions are mandatory while other programs define additional functions. Functions are declared with a return type, name, and parameters.
2) Functions are defined with a syntax including the return type, name, parameters, and body. Functions can be called within other functions or the main function by passing values.
3) Inline functions have their code placed at the call site at compile time to avoid function call overhead. They are defined using the inline keyword before the return type for small, single line functions.
4) Functions can have default arguments which are values provided in the declaration that are used if not passed to the function. They must
The document discusses inline functions in C++. Inline functions allow code from a function to be pasted directly into the call site rather than executing a function call. This avoids overhead from calling and returning from functions. Good candidates for inline are small, simple functions called frequently. The document provides an example of a function defined with the inline keyword and the optimizations a compiler may perform after inlining. It also compares inline functions to macros and discusses where inline functions are best used.
The document discusses inline functions in C++. It explains that inline functions are replaced by their function code during compilation so they run faster than regular functions but use more memory. It provides examples of declaring and defining inline functions and demonstrates how an inline function call is handled at runtime by replacing it with the function statements. The document also discusses making member functions inline within a class declaration.
CSEC Mathematics Review - Introduction To Functions & RelationsKevin Small
www.knowledgescroll.com
After you view this presentation and watch the accompanying video you should be able to:
Define what is a Function
Explain How Function Notation Works
Define What is a Relation
Define The Terms Domain and Range
Learn How To Draw Mapping Diagrams
Learn The Different Types of Relations
Lean How To Test For Functions
In this session we will go over the fundamentals of functional programming and see how functional programming can help make our code more reusable, stable, scalable and fun.
A talk from CppEurope 2019 about functional programming in C++.
It talks about lambdas, immutability, operations with functions (partial application, currying, functional composition), shows an example and ends with a procedure for refactoring legacy code.
Functional programming uses pure functions that have no side effects to make code more predictable, stable, and easier to test and maintain. It favors immutability over mutable state to avoid side effects. Complex programs are built by composing simple functions together rather than using complex objects. Higher order functions like map, filter, and reduce help avoid loops. Currying allows functions to take parameters one at a time for simpler composition.
INTRODUCTION
COMPARISON BETWEEN NORMAL FUNCTION AND INLINE FUNCTION
PROS AND CONS
WHY WHEN AND HOW TO USED?
GENERAL STRUCTURE OF INLINE FUNCTION
EXAMPLE WITH PROGRAM CODE
The document discusses different types of functions in C++ including:
1) Main functions are mandatory while other programs define additional functions. Functions are declared with a return type, name, and parameters.
2) Functions are defined with a syntax including the return type, name, parameters, and body. Functions can be called within other functions or the main function by passing values.
3) Inline functions have their code placed at the call site at compile time to avoid function call overhead. They are defined using the inline keyword before the return type for small, single line functions.
4) Functions can have default arguments which are values provided in the declaration that are used if not passed to the function. They must
The document discusses inline functions in C++. Inline functions allow code from a function to be pasted directly into the call site rather than executing a function call. This avoids overhead from calling and returning from functions. Good candidates for inline are small, simple functions called frequently. The document provides an example of a function defined with the inline keyword and the optimizations a compiler may perform after inlining. It also compares inline functions to macros and discusses where inline functions are best used.
The document discusses inline functions in C++. It explains that inline functions are replaced by their function code during compilation so they run faster than regular functions but use more memory. It provides examples of declaring and defining inline functions and demonstrates how an inline function call is handled at runtime by replacing it with the function statements. The document also discusses making member functions inline within a class declaration.
CSEC Mathematics Review - Introduction To Functions & RelationsKevin Small
www.knowledgescroll.com
After you view this presentation and watch the accompanying video you should be able to:
Define what is a Function
Explain How Function Notation Works
Define What is a Relation
Define The Terms Domain and Range
Learn How To Draw Mapping Diagrams
Learn The Different Types of Relations
Lean How To Test For Functions
The document provides an overview of functional programming concepts including:
- Functions are the primary building blocks and avoid side effects by being pure and using immutable data.
- Referential transparency means functions always return the same output for a given input.
- Higher order functions accept or return other functions. Function composition combines functions.
- Partial application and currying transform functions to accept arguments incrementally.
Here is how to convert the method into a curried function:
Function<A, Function<B, Function<C, Function<D, String>>>> func =
a -> b -> c -> d -> String.format("%s, %s, %s, %s", a, b, c, d);
This defines func as a function that takes an argument of type A and returns a function that takes B and returns a function that takes C and returns a function that takes D and returns a String.
This document discusses functions in C++. It defines functions as modules that divide programs into smaller, more manageable pieces. It covers function prototypes, call by reference, return by reference, inline functions, default arguments, constant arguments, recursion, function overloading, friend and virtual functions, and math library functions. The main points are that every C++ program must have a main function, functions can pass arguments by reference to modify the original variables, and functions allow breaking programs into reusable and modular components.
Inline functions in C++ allow the compiler to paste the function code directly where the function is called, rather than generating a call to a function definition elsewhere. Inline functions can improve performance by reducing compilation time and function call overhead. However, functions containing loops may not be expanded inline and will generate a warning, as loop variables have dynamic rather than static values. To resolve this, the function can be declared outside the class using the scope resolution operator.
Functions allow programmers to structure C++ programs into modular segments of code to perform individual tasks. There are two types of functions: library functions and user-defined functions. User-defined functions are defined using a return type, function name, and parameters. Functions can be called by value or by reference and can also be inline, recursive, or friend functions.
Functions in C++, this presentation will cover the following topics
• Functions
• Functions Basics
• Overloaded functions
o Different numbers of arguments
o Different kinds of arguments
Revision Fucntion overloading
• Inline functions
• Default arguments
Inline functions allow the compiler to replace function calls with the function's definition during compilation. This avoids overhead from function calls and improves execution speed. Inline functions can increase executable size due to code expansion. They are best for small functions where performance is critical, and care needs to be taken to avoid excessive code bloat.
1. Inline functions are small functions whose code is inserted at the call site instead of generating a function call. This avoids overhead of function calls but increases code size.
2. Function overloading allows different functions to have the same name but different parameters. The compiler determines which version to call based on argument types.
3. C++ classes allow defining data types that bundle together data members and member functions that can access them. Classes support data hiding and inheritance.
It tells about functions in C++,Types,Use,prototype,declaration,Arguments etc
function with
A function with no parameter and no return value
A function with parameter and no return value
A function with parameter and return value
A function without parameter and return value
Call by value and address
The document discusses functional programming concepts like single-assignment, recursion, and immutable data structures. It provides examples of recursion in functions like factorial and selection sort. It compares iterative and recursive looping approaches and discusses advantages of functional programming like avoiding side effects and simplifying debugging.
This document discusses C++ functions. It defines a function as a group of statements that is given a name and can be called from within a program. The structure of a C++ function includes a header and body. The header specifies the return type, name, and parameters, while the body contains the code. Functions can use value, reference, and constant reference parameters. Variables within a function can be local or global. Standard library functions are pre-defined in headers like <iostream> and <math.h>. The document provides examples of defining, calling, and using different types of functions and parameters in C++.
Operator overloading allows operators like + and - to be used with user-defined types by defining corresponding operator functions. These functions have a return type and take class objects or references as arguments, and implement the desired operation. Only existing operators can be overloaded, and their basic meaning cannot change. Certain operators like . and :: cannot be overloaded. Overloaded operators follow syntax rules and can be invoked using operator notation.
Inline functions are functions that are declared with the inline keyword or within a class. The compiler may optimize inline functions by copying the function's body where it is called to reduce execution time by eliminating function call overhead. To be considered inline, a function must be short, non-recursive, contain no return statements or static variables, and be declared within a class. Inline functions can shorten execution time but increase file size and potentially make code less readable.
This document discusses various aspects of functions in C++ including function prototypes, definitions, calls, overloading, pointers, callbacks, and templates. It provides examples and explanations of each concept. The key topics covered are:
- Function prototypes declare a function's name, return type, and parameters.
- Definitions implement what a function does through code within curly braces.
- Functions are called by name with appropriate arguments.
- Overloading allows different functions to have the same name based on different parameters.
- Function pointers allow functions to be passed as arguments to other functions.
- Callback functions are functions that are passed as arguments to be called later.
- Templates define functions that operate on different data
This document discusses function parameters in Python. It explains that parameters hold the values passed into a function as arguments. Parameters act as variables that accept the values passed during a function call. Functions can have multiple parameters, and the number and order of arguments passed must match the function definition. Arguments are passed by value, so changes to a parameter do not affect the original argument. A function can return multiple values using return, and functions themselves can be passed as parameters to other functions.
Inline functions allow function code to be expanded at the point of call at compile time for improved performance. They are declared with the "inline" keyword and behave like regular functions but with type checking and argument evaluation. Macros are preprocessor directives that perform simple textual substitution and lack type checking, so they are more error prone than inline functions. The key differences between inline functions and macros are that inline functions are parsed by the compiler while macros are expanded by the preprocessor, and inline functions support features like debugging that macros do not.
This document discusses parameters in C++. There are two types of parameters: formal parameters defined in a function and actual parameters passed during a function call. C++ supports two ways of passing parameters: call by value where the formal parameter is a copy of the actual value, and call by reference where the formal parameter is an alias to the actual parameter. Call by reference allows a function to modify the original value. While it is more efficient for large data types, it can be ambiguous whether a parameter is intended for input or output.
This document discusses C++ functions. It begins by defining what a function is and describing standard and user-defined functions. It then covers the structure of C++ functions including the function signature, parameters, return values, and body. Examples are provided of defining, declaring, calling and overloading functions. The document also discusses scope of variables, passing data between functions, and inline functions.
This presentation explains the difference between concurrency and parallelism, and how could we make parallel computations and how could we design an API for parallel computation following the structure presented in the Functional Programming in Scala book
Functional Programming Patterns for the Pragmatic ProgrammerRaúl Raja Martínez
In this talk we will see a pragmatic approach to building a purely functional architecture that delivers cohesive functional components.
We will cover functional patterns such as Free Monads, Transformers, Kleisli arrows, dependently typed checked exceptions
and types as well as how they can be glued together to achieve pure functions that are composable, context free, dependently injectable and testable.
Dome project and code with instructions to run it can be found at:
https://github.com/47deg/func-architecture
The document provides an overview of functional programming concepts including:
- Functions are the primary building blocks and avoid side effects by being pure and using immutable data.
- Referential transparency means functions always return the same output for a given input.
- Higher order functions accept or return other functions. Function composition combines functions.
- Partial application and currying transform functions to accept arguments incrementally.
Here is how to convert the method into a curried function:
Function<A, Function<B, Function<C, Function<D, String>>>> func =
a -> b -> c -> d -> String.format("%s, %s, %s, %s", a, b, c, d);
This defines func as a function that takes an argument of type A and returns a function that takes B and returns a function that takes C and returns a function that takes D and returns a String.
This document discusses functions in C++. It defines functions as modules that divide programs into smaller, more manageable pieces. It covers function prototypes, call by reference, return by reference, inline functions, default arguments, constant arguments, recursion, function overloading, friend and virtual functions, and math library functions. The main points are that every C++ program must have a main function, functions can pass arguments by reference to modify the original variables, and functions allow breaking programs into reusable and modular components.
Inline functions in C++ allow the compiler to paste the function code directly where the function is called, rather than generating a call to a function definition elsewhere. Inline functions can improve performance by reducing compilation time and function call overhead. However, functions containing loops may not be expanded inline and will generate a warning, as loop variables have dynamic rather than static values. To resolve this, the function can be declared outside the class using the scope resolution operator.
Functions allow programmers to structure C++ programs into modular segments of code to perform individual tasks. There are two types of functions: library functions and user-defined functions. User-defined functions are defined using a return type, function name, and parameters. Functions can be called by value or by reference and can also be inline, recursive, or friend functions.
Functions in C++, this presentation will cover the following topics
• Functions
• Functions Basics
• Overloaded functions
o Different numbers of arguments
o Different kinds of arguments
Revision Fucntion overloading
• Inline functions
• Default arguments
Inline functions allow the compiler to replace function calls with the function's definition during compilation. This avoids overhead from function calls and improves execution speed. Inline functions can increase executable size due to code expansion. They are best for small functions where performance is critical, and care needs to be taken to avoid excessive code bloat.
1. Inline functions are small functions whose code is inserted at the call site instead of generating a function call. This avoids overhead of function calls but increases code size.
2. Function overloading allows different functions to have the same name but different parameters. The compiler determines which version to call based on argument types.
3. C++ classes allow defining data types that bundle together data members and member functions that can access them. Classes support data hiding and inheritance.
It tells about functions in C++,Types,Use,prototype,declaration,Arguments etc
function with
A function with no parameter and no return value
A function with parameter and no return value
A function with parameter and return value
A function without parameter and return value
Call by value and address
The document discusses functional programming concepts like single-assignment, recursion, and immutable data structures. It provides examples of recursion in functions like factorial and selection sort. It compares iterative and recursive looping approaches and discusses advantages of functional programming like avoiding side effects and simplifying debugging.
This document discusses C++ functions. It defines a function as a group of statements that is given a name and can be called from within a program. The structure of a C++ function includes a header and body. The header specifies the return type, name, and parameters, while the body contains the code. Functions can use value, reference, and constant reference parameters. Variables within a function can be local or global. Standard library functions are pre-defined in headers like <iostream> and <math.h>. The document provides examples of defining, calling, and using different types of functions and parameters in C++.
Operator overloading allows operators like + and - to be used with user-defined types by defining corresponding operator functions. These functions have a return type and take class objects or references as arguments, and implement the desired operation. Only existing operators can be overloaded, and their basic meaning cannot change. Certain operators like . and :: cannot be overloaded. Overloaded operators follow syntax rules and can be invoked using operator notation.
Inline functions are functions that are declared with the inline keyword or within a class. The compiler may optimize inline functions by copying the function's body where it is called to reduce execution time by eliminating function call overhead. To be considered inline, a function must be short, non-recursive, contain no return statements or static variables, and be declared within a class. Inline functions can shorten execution time but increase file size and potentially make code less readable.
This document discusses various aspects of functions in C++ including function prototypes, definitions, calls, overloading, pointers, callbacks, and templates. It provides examples and explanations of each concept. The key topics covered are:
- Function prototypes declare a function's name, return type, and parameters.
- Definitions implement what a function does through code within curly braces.
- Functions are called by name with appropriate arguments.
- Overloading allows different functions to have the same name based on different parameters.
- Function pointers allow functions to be passed as arguments to other functions.
- Callback functions are functions that are passed as arguments to be called later.
- Templates define functions that operate on different data
This document discusses function parameters in Python. It explains that parameters hold the values passed into a function as arguments. Parameters act as variables that accept the values passed during a function call. Functions can have multiple parameters, and the number and order of arguments passed must match the function definition. Arguments are passed by value, so changes to a parameter do not affect the original argument. A function can return multiple values using return, and functions themselves can be passed as parameters to other functions.
Inline functions allow function code to be expanded at the point of call at compile time for improved performance. They are declared with the "inline" keyword and behave like regular functions but with type checking and argument evaluation. Macros are preprocessor directives that perform simple textual substitution and lack type checking, so they are more error prone than inline functions. The key differences between inline functions and macros are that inline functions are parsed by the compiler while macros are expanded by the preprocessor, and inline functions support features like debugging that macros do not.
This document discusses parameters in C++. There are two types of parameters: formal parameters defined in a function and actual parameters passed during a function call. C++ supports two ways of passing parameters: call by value where the formal parameter is a copy of the actual value, and call by reference where the formal parameter is an alias to the actual parameter. Call by reference allows a function to modify the original value. While it is more efficient for large data types, it can be ambiguous whether a parameter is intended for input or output.
This document discusses C++ functions. It begins by defining what a function is and describing standard and user-defined functions. It then covers the structure of C++ functions including the function signature, parameters, return values, and body. Examples are provided of defining, declaring, calling and overloading functions. The document also discusses scope of variables, passing data between functions, and inline functions.
This presentation explains the difference between concurrency and parallelism, and how could we make parallel computations and how could we design an API for parallel computation following the structure presented in the Functional Programming in Scala book
Functional Programming Patterns for the Pragmatic ProgrammerRaúl Raja Martínez
In this talk we will see a pragmatic approach to building a purely functional architecture that delivers cohesive functional components.
We will cover functional patterns such as Free Monads, Transformers, Kleisli arrows, dependently typed checked exceptions
and types as well as how they can be glued together to achieve pure functions that are composable, context free, dependently injectable and testable.
Dome project and code with instructions to run it can be found at:
https://github.com/47deg/func-architecture
The document discusses function composition and recursion in JavaScript. It introduces basic functions like double, square, and inc. It then shows how to work with these functions in an imperative style using a for loop, and in a declarative style using map. It discusses how composition allows building complexity by combining functions. The document also discusses combinators, introducing birds that represent functions like identity, mockingbird, bluebird (compose), lark, and meadowlark. It concludes by explaining the Y-combinator and how it allows recursion using fixed points.
Introduction to Functional Programming with Scalapramode_ce
The document provides an introduction to functional programming with Scala. It outlines the following topics that will be covered: learning Scala syntax and writing simple programs; important functional programming concepts like closures, higher-order functions, purity, lazy evaluation, currying, tail calls, immutability, and type inference; and understanding the functional programming paradigm through Scala. It also provides some background information on Scala and examples of Scala code demonstrating various concepts.
Principles of functional progrmming in scalaehsoon
a short outline on necessity of functional programming and principles of functional programming in Scala.
In the article some keyword are used but not explained (to keep the article short and simple), the interested reader can look them up in internet.
Using Akka futures
Akka Futures definition: A Future is a data structure used to retrieve of some concurrentoperationThis operation is performed by an Actor or a dispatcher directly result can be accessed synchronously or asynchronously.
This document discusses Akka futures and remote actors. It covers how to use futures to retrieve results asynchronously from actors or directly. It also describes how to enable remote capabilities in an Akka project, how to create remote actors, and how to look up remote actors. Functional composition of futures using map, filter, and for comprehensions is also covered.
Game of Life - Polyglot FP - Haskell - Scala - Unison - Part 3Philip Schwarz
(download for picture-perfect quality) Follow along as Trampolining is used to overcome Stack Overflow issues with the simple IO monad, deepening you understanding of the IO monad in the process. See Game of Life IO actions migrated to the Cats Effect IO monad, which is trampolined in its flatMap evaluation.
Errata:
slide 33: "impure factorial function" should be "initial factorial function"
slide 34: there is a pointless short vertical bar inside a speech bubble
slide 39: "it is till annoying" should be "it is still annoying"
slide 44: "values that we are interested" should be "values that we are interested in"
Scala has both methods and functions in Scala. We refer them interchangeably but there are situations where the difference matters. In the slides, we'll talk about Eta expansion in scala.
Functional Programming Concepts for Imperative ProgrammersChris
The document discusses functional programming concepts including the origins of the λ-calculus and Lisp. It covers functions as data, lambda expressions, closures, function composition, and higher-order functions. Examples are provided in JavaScript and Scala of implementing functions like fold to operate on lists. While many functional concepts are covered, topics like currying, monads, and lazy evaluation are noted but not discussed in detail.
i. The linear convolution of two sequences was calculated using the conv command in MATLAB. The input sequences, individual sequences, and convolved output were plotted.
ii. Linear convolution was also calculated using the DFT and IDFT. The sequences were padded with zeros and transformed to the frequency domain using FFT. The transformed sequences were multiplied and inverse transformed using IFFT to obtain the circular convolution result.
iii. The circular convolution result using DFT/IDFT was the same as the linear convolution using the conv command, demonstrating the equivalence between linear and circular convolution in the frequency domain.
This lab manual covers MATLAB and digital signal processing concepts. It includes:
1) An introduction to MATLAB including basic commands, functions, vectors, matrices and operations.
2) Digital signal processing concepts like sampling, discrete time signals, linear convolution using the conv command are explained.
3) Experiments are included to verify the sampling theorem and study linear convolution of sequences.
The document discusses asynchronous programming with futures and promises in Scala. It covers key topics like:
1) Futures provide a way to reason about parallel operations efficiently in a non-blocking way. Futures represent asynchronous computations with results that can be accessed later.
2) Promises allow completing a future with a value or exception. Callbacks allow accessing future results asynchronously once completed.
3) Functional composition with futures uses methods like map, flatMap, recover to transform and compose futures. For-comprehensions provide a cleaner way to compose futures.
Introduces the functional programming ideas of Functor, Apply, Applicative And Monad. Shows how to implement each in Scala with Scalaz and how to validate the implementation using property based test using specs2 and scalacheck.
The document summarizes key points from a lecture on Scala programming:
1. Implicits allow defining implicit conversions to resolve type mismatches and fix compiler errors. Monads separate composition timeline from execution and allow computations to carry extra data.
2. The Option type in Scala is equivalent to Haskell's Maybe monad. It provides flatMap and map operations for monadic computations.
3. Scala supports parallel collections for parallelism and futures for composable concurrent programming. Futures are monads that can be operated on and composed asynchronously.
(1) The document provides instructions for installing the CounterClockwise plugin for Eclipse to get an IDE for Clojure development. (2) It describes how to create and load Clojure files and launch a REPL for evaluation. (3) The document includes exercises on Clojure basics like functions, macros, and functional programming techniques as well as examples for implementing macros.
This document provides an overview of a lecture on functional programming in Scala. It covers the following topics:
1. A recap of functional programming principles like functions as first-class values and no side effects.
2. An introduction to the Haskell programming language including its syntax for defining functions.
3. How functions are defined in Scala and how they are objects at runtime.
4. Examples of defining the factorial function recursively in Haskell and Scala, and making it tail recursive.
5. Concepts of first-class functions, currying, partial application, and an example of implementing looping in Scala using these techniques.
This document summarizes key parts of Java 8 including lambda expressions, method references, default methods, streams API improvements, removal of PermGen space, and the new date/time API. It provides code examples and explanations of lambda syntax and functional interfaces. It also discusses advantages of the streams API like lazy evaluation and parallelization. Finally, it briefly outlines the motivation for removing PermGen and standardizing the date/time API in Java 8.
This document provides an introduction to the Java programming language. It discusses Java's evolution and history from 1991 to present. It also covers Java fundamentals including data types, operators, decision making and looping constructs, classes and objects, arrays and strings. The document is intended as an overview of the major topics and features in Java.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Unlock the Future of Search with MongoDB Atlas_ Vector Search Unleashed.pdfMalak Abu Hammad
Discover how MongoDB Atlas and vector search technology can revolutionize your application's search capabilities. This comprehensive presentation covers:
* What is Vector Search?
* Importance and benefits of vector search
* Practical use cases across various industries
* Step-by-step implementation guide
* Live demos with code snippets
* Enhancing LLM capabilities with vector search
* Best practices and optimization strategies
Perfect for developers, AI enthusiasts, and tech leaders. Learn how to leverage MongoDB Atlas to deliver highly relevant, context-aware search results, transforming your data retrieval process. Stay ahead in tech innovation and maximize the potential of your applications.
#MongoDB #VectorSearch #AI #SemanticSearch #TechInnovation #DataScience #LLM #MachineLearning #SearchTechnology
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
20 Comprehensive Checklist of Designing and Developing a WebsitePixlogix Infotech
Dive into the world of Website Designing and Developing with Pixlogix! Looking to create a stunning online presence? Look no further! Our comprehensive checklist covers everything you need to know to craft a website that stands out. From user-friendly design to seamless functionality, we've got you covered. Don't miss out on this invaluable resource! Check out our checklist now at Pixlogix and start your journey towards a captivating online presence today.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Goodbye Windows 11: Make Way for Nitrux Linux 3.5.0!SOFTTECHHUB
As the digital landscape continually evolves, operating systems play a critical role in shaping user experiences and productivity. The launch of Nitrux Linux 3.5.0 marks a significant milestone, offering a robust alternative to traditional systems such as Windows 11. This article delves into the essence of Nitrux Linux 3.5.0, exploring its unique features, advantages, and how it stands as a compelling choice for both casual users and tech enthusiasts.
1. Beyond Mere Actors
Concurrent Functional
Programming with Scalaz
Rúnar Bjarnason
2. Traditional Java Concurrency
Manual creation of threads
Manual synchronisation
One thread per process
Processes communicate by shared mutable state
3. Traditional Java Concurrency
Manual creation of threads
Manual synchronisation
One thread per process
Processes communicate by shared mutable state
Problem: Threads do not compose
4. java.util.concurrent
Since JDK 5
Includes useful building blocks for making higher-level
abstractions.
Atomic references
Countdown latches
ExecutorService
Callable
Future
6. Futures
ExecutorService is a means of turning Callable[A] into Future
[A]
implicit def toCallable[A](a: => A) =
new Callable[A] { def call = a }
implicit val s = Executors.newCachedThreadPool
e1 and e2 are evaluated concurrently
val x = s submit { e1 }
val y = e2
Futures can participate in expressions:
val z = f(x.get, y)
No mention of threads in this code. No shared state.
7. Futures
There's a serious problem. How would we implement this
function?
def fmap[A,B](fu: Future[A], f: A => B)
(implicit s: ExecutorService):Future[B] =
8. Futures
We have to call Future.get, blocking the thread.
def fmap[A,B](fu: Future[A], f: A => B)
(implicit s: ExecutorService):Future[B] =
s submit f(fu.get)
This is a barrier to composition.
Futures cannot be composed without blocking a thread.
9. scalaz.concurrent
Strategy - Abstracts over ways of evaluating expressions
concurrently.
Actor - Light-weight thread-like process, communicates by
asynchronous messaging.
Promise - Compose concurrent functions.
10. Parallel Strategies
ExecutorService: Callable[A] => Future[A]
Callable[A] ~= Function0[A]
Future[A] ~= Function0[A]
Also written: () => A
Strategy[A] ~= Function0[A] => Function0[A]
Also written: (() => A) => () => A
Turns a lazy expression of type A into an expression of the
same type.
11. scalaz.concurrent.Strategy
Separates the concerns of parallelism and algorithm.
Captures some threading pattern and isolates the rest of your
code from threading.
Executor - wraps the expression in a Callable, turns it into a
Future via an implicit ExecutorService.
Naive - Starts a new thread for each expression.
Sequential - Evaluates each expression in the current thread
(no concurrency).
Identity - Performs no evaluation at all.
12. Parallel Strategies
You can write your own Strategies.
Some (crazy) ideas:
Delegate to the fork/join scheduler.
Run in the Swing thread.
Call a remote machine.
Ask a human, or produce a random result.
13. Actors
Provide asynchronous communication among processes.
Processes receive messages on a queue.
Messages can be enqueued with no waiting.
An actor is always either suspended (waiting for messages) or
working (acting on a message).
An actor processes messages in some (but any) order.
14. scalaz.concurrent.Actor
These are not scala.actors. Differences:
Simpler. Scalaz Actors are distilled to the essentials.
Messages are typed.
Actor is sealed, and instantiated by supplying:
type A
effect: A => Unit
(implicit) strategy: Strategy[Unit]
(Optional) Error Handler: Throwable => Unit
Strategy + Effect = Actor
16. Actor: Contravariant Cofunctor
An actor can be composed with a function:
x.comap(f)
Comap has this type:
comap: (B => A) => Actor[A] => Actor[B]
Returns a new actor that applies f to its messages and sends
the result to actor x.
x comap f is equivalent to x compose f, but results in an
Actor, as opposed to a function.
18. Problems with Actors
You have to think about state and process communication.
An actor can (must?) expose its state.
It's all about side-effects!
Side-effects absolutely do not compose.
You cannot compose Actors with each other.
Actor[A] ~= (A => Unit)
There's not a lot you can do with Unit.
20. scalaz.concurrent.Promise
Constructed by giving an expression to promise:
lazy val e:String = {Thread sleep 5000; "Hi."}
val p: Promise[String] = promise(e)
Takes an implicit Strategy[Unit]. The expression is
evaluated concurrently by the Strategy.
Think of this as forking a process.
The result is available later by calling p.get. This blocks the
current thread.
But we never have to call it!
21. On Time-Travel
Promised values are available in the future.
What does it mean to get a value out of the future?
Time-travel into the future is easy. Just wait.
But we don't have to go into the future.
We can give our future-selves instructions.
Instead of getting values out of the future, we send
computations into the future.
22. Lifting a function into the future
Consider: promise(e).map(f)
map has the following type:
(A => B) => Promise[A] => Promise[B]
We take an ordinary function and turn it into a function that
operates on Promises.
It's saying: Evaluate e concurrently, applying f to the result
when it's ready. Returns a Promise of the final result.
23. Composing concurrent functions
A concurrent function is of the type A => Promise[B]
Syntax sugar to make any function a concurrent function:
val g = f.promise
promise(f: A => B) = (a:A) => promise(f(a))
We can bind the arguments of concurrent functions to promised
values, using flatMap:
promise(e).flatMap(g)
flatMap has this type:
(A => Promise[B]) => Promise[A] => Promise[B]
24. Composing concurrent functions
We can compose concurrent functions with each other too.
If f: A => Promise[B] and g: B => Promise[C] then
(f >=> g): A => Promise[C]
(f >=> g)(x) is equivalent to f(x) flatMap g
25. Joining Promises
join[A]: Promise[Promise[A]] => Promise[A]
(promise { promise { expression } }).join
Join removes the "inner brackets".
A process that forks other processes can join with them later,
without synchronizing or blocking.
A process whose result depends on child processes is still just
a single Promise, and thus can run in a single thread.
Therefore, pure promises cannot deadlock or starve.
27. But wait, there's more!
Parallel counterparts of map, flatMap, and zipWith:
parMap, parFlatMap, and parZipWith
x.parMap(f)
Where x can be a List, Stream, Function, Option, Promise, etc.
Scalaz provides parMap on any Functor.
parZipWith for parallel zipping of any Applicative Functor.
parFlatMap is provided for any Monad.
29. Advanced Topics
Functor is simply this interface:
trait Functor[F[_]] {
def fmap[A, B](r: F[A], f: A => B): F[B]
}
Functors are "mappable". Any implementation of this interface
is a functor. Here's the Promise functor:
new Functor[Promise] {
def fmap[A, B](t: Promise[A], f: A => B) =
t.flatMap(a => promise(f(a)))
}
30. Advanced Topics
Monad is simply this interface:
trait Monad[M[_]] extends Functor[M] {
fork[A](a: A): M[A]
join[A](a: M[M[A]]): M[A]
}
Monads are fork/map/joinable. Any implementation of this
interface is a monad. Here's the Promise monad:
new Monad[Promise] {
def fork[A](a: A) = promise(a)
def join[A](a: Promise[Promise[A]]) =
a.flatMap(Functions.identity)
}
31. Welcome to Scalaz
Scalaz is a general-purpose library for higher-order
programming.
There's a lot here. Go play around, and ask questions on the
Scalaz Google Group.
For more information:
http://code.google.com/p/scalaz
Documentation is lacking, but we're working on that.
A release of Scalaz 5.0 will coincide with a release of Scala
2.8.