LISP, an acronym for list processing, is a programming language that was designed for easy manipulation of data strings. It is a commonly used language for artificial intelligence (AI) programming.
This document provides an overview of the Lisp programming language. It begins with some notable quotes about Lisp praising its power and importance. It then covers the basic syntax of Lisp including its use of prefix notation, basic data types like integers and booleans, and variables. It demonstrates how to print, use conditional statements like IF and COND, and describes lists as the core data structure in Lisp.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It was closely tied to early AI research and used in systems like SHRDLU. Lisp allows programs to treat code as data through homoiconicity and features like lambdas, conses, and list processing functions make it good for symbolic and functional programming.
Prolog is a declarative logic programming language where programs consist of facts and rules. Facts are terms that are always true, while rules define relationships between terms using logic notation "if-then". A Prolog program is run by asking queries of the program's database. Variables must start with an uppercase letter and are used to represent unknown values, while atoms are constants that represent known values.
The document provides an introduction to the Lisp programming language. It begins with an overview of Lisp and discusses its key features: it is a list processing language where lists are the basic data structure; it is functional in nature; and it uses interpretation rather than compilation. The document then covers Lisp basics like data types, evaluation rules, defining functions, conditional statements, loops, and input/output operations. It also introduces some common Lisp functions and techniques like car, cdr, cons, append, cond, do, dotimes, and dolist.
This presentation provides an overview of various programming paradigms including imperative, declarative, functional, object-oriented, and multi-paradigm. It discusses the basic concepts and definitions of each paradigm, provides examples of commonly used languages, and briefly compares the different approaches. The presentation concludes that while no consensus exists on the best paradigm, procedural and object-oriented paradigms using languages like C, C++, and Java tend to be most popular for introductory courses.
Lisp is a functional programming language where the basic data structure is linked lists and atoms. It was one of the earliest programming languages developed in 1958. Lisp programs are run by interacting with an interpreter like Clisp. Key aspects of Lisp include its use of prefix notation, treating all code as nested lists, defining functions using defun, and its emphasis on recursion and higher-order functions. Common control structures include cond for conditional evaluation and looping constructs like loop. Lisp fell out of widespread use due to performance issues with interpretation and low interoperability with other languages.
The document discusses the differences between recursion and iteration. Recursion involves a method calling itself, with each call reducing the problem size until a base case is reached. Iteration uses loops to repeat a process. Examples include calculating factorials, Fibonacci numbers, and binary search recursively and iteratively. Some problems like directory traversal are simpler using recursion due to its divide-and-conquer approach, while iterations may be preferred for easier explanation or to avoid stack overflows with deep recursions.
Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
Automata theory studies abstract computing devices and the types of tasks they are capable of. Alan Turing pioneered this field in the 1930s by studying Turing machines. The theory examines questions like which tasks can and cannot be performed by different models of machines. It also considers the distinction between what is computable versus the complexity of computation. Common concepts include finite automata, formal languages, and the Chomsky hierarchy for classifying language types. Proofs in automata theory involve techniques like deduction, induction, contradiction, and establishing results by definition.
This presentation educates you about Python - GUI Programming(Tkinter), Tkinter Programming with syntaxe example, Tkinter Widgets with Operator & Description, Standard attributes.
For more topics stay tuned with learnbay.
Yacc is a general tool for describing the input to computer programs. It generates a LALR parser that analyzes tokens from Lex and creates a syntax tree based on the grammar rules specified. Yacc was originally developed in the 1970s and generates C code for the syntax analyzer from a grammar similar to BNF. It has been used to build compilers for languages like C, Pascal, and APL as well as for other programs like document retrieval systems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Lex is a program generator designed for lexical processing of character input streams. It works by translating a table of regular expressions and corresponding program fragments provided by the user into a program. This program then reads an input stream, partitions it into strings matching the given expressions, and executes the associated program fragments in order. Flex is a fast lexical analyzer generator that is an alternative to Lex. It generates scanners that recognize lexical patterns in text based on pairs of regular expressions and C code provided by the user.
The document provides information about shells in Linux operating systems. It defines what a kernel and shell are, explains why shells are used, describes different types of shells, and provides examples of shell scripting. The key points are:
- The kernel manages system resources and acts as an intermediary between hardware and software. A shell is a program that takes commands and runs them, providing an interface between the user and operating system.
- Shells are useful for automating tasks, combining commands to create new ones, and adding functionality to the operating system. Common shells include Bash, Bourne, C, Korn, and Tcsh.
- Shell scripts allow storing commands in files to automate tasks.
The document discusses recursion through examples such as calculating factorials and multiplying numbers recursively. It defines recursion as solving a problem by solving smaller instances of the same problem. Recursive algorithms break down a problem into smaller sub-problems until reaching a base case, and use the solutions to the sub-problems to solve the original problem.
The document discusses the Lisp programming language. It notes that Allegro Common Lisp will be used and lists textbooks for learning Lisp. It provides 10 points on Lisp, including that it is interactive, dynamic, uses symbols and lists as basic data types, prefix notation for operators, and classifies different data types. Evaluation follows simple rules and programs can be treated as both instructions and data.
This document discusses assembly language and assemblers. It begins by explaining that assembly language provides a more readable and convenient way to program compared to machine language. It then describes how an assembler works, translating assembly language programs into machine code. The elements of assembly language are defined, including mnemonic operation codes, symbolic operands, and data declarations. The document also covers instruction formats, sample assembly language programs, and the processing an assembler performs to generate machine code from assembly code.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
In this PPT you will learn how to use looping in python.
For more presentation in any subject please contact us on
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You get a new presentation every Sunday at 10 AM.
Learn more about Python by clicking on given below link
Python Introduction- https://www.slideshare.net/RaginiJain21/final-presentation-on-python
Basic concept of Python -https://www.slideshare.net/RaginiJain21/python-second-ppt
Python Datatypes - https://www.slideshare.net/RaginiJain21/data-types-in-python-248466302
Python Library & Module - https://www.slideshare.net/RaginiJain21/python-libraries-and-modules
Basic Python Programs- https://www.slideshare.net/RaginiJain21/basic-python-programs
Python Media Libarary - https://www.slideshare.net/RaginiJain21/python-media-library
Recursion is defined as a technique where a function calls itself, either directly or indirectly. It involves breaking a problem down into smaller sub-problems until it reaches a base case. For recursion to work properly, it needs a base case where the problem can be solved without further recursion, and each recursive call must make progress towards the base case. While recursion can provide elegant solutions, it uses more memory and time than iterative approaches like loops. Common pitfalls are not having a base case, not making progress on each call, or using too many resources on each recursive call.
Regular expressions are a powerful tool for searching, matching, and parsing text patterns. They allow complex text patterns to be matched with a standardized syntax. All modern programming languages include regular expression libraries. Regular expressions can be used to search strings, replace parts of strings, split strings, and find all occurrences of a pattern in a string. They are useful for tasks like validating formats, parsing text, and finding/replacing text. This document provides examples of common regular expression patterns and methods for using regular expressions in Python.
This document discusses language processors and their fundamentals. It begins by explaining the semantic gap between how software is designed and implemented, and how language processors help bridge this gap. It then covers different types of language processors like translators, interpreters, and preprocessors. The key activities of language processors - analysis and synthesis - are explained. Analysis includes lexical, syntax and semantic analysis, while synthesis includes memory allocation and code generation. Language specifications using grammars and different binding times are also covered. Finally, common language processing development tools like LEX and YACC are introduced.
Pseudocode is an outline of a program written in a way that can be easily converted to a programming language. It uses natural language and programming constructs like expressions, methods, decision structures, loops, and array indexing. The three essential programming constructs are selection (if/else), repetition (while loops), and sequence. Pseudocode also uses relational, logical, and arithmetic operators and follows rules like using meaningful names and indenting code blocks.
This presentation contains:
1. Introduction
2. Central areas of TOC
3. Complexity theory
4. Computability theory
5. Automata theory
6. Related terminologies
7. Strings
8. Languages
9. Proof, Theorem, Lemma, Corollaries
The document discusses recursive functions and provides examples of recursive algorithms for calculating factorial, greatest common divisor (GCD), Fibonacci numbers, power functions, and solving the Towers of Hanoi problem. Recursive functions are functions that call themselves during their execution. They break down problems into subproblems of the same type until reaching a base case. This recursive breakdown allows problems to be solved in a top-down, step-by-step manner.
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
This document provides an overview of the LISP programming language. It discusses LISP syntax, data types, functions, predicates, conditionals, variables, and other core concepts. Some key points:
- LISP uses prefix notation and parentheses extensively. Functions are applied using (func arg1 arg2).
- Core data types include numbers, symbols, strings, and lists. Lists are a fundamental data structure and there are many functions for manipulating them.
- Functions are defined using defun and can take parameters. Predicates test conditions and return t or nil.
- Conditionals include cond, if, when, and case. Assignment is done with setq.
-
Algorithms Lecture 1: Introduction to AlgorithmsMohamed Loey
We will discuss the following: Algorithms, Time Complexity & Space Complexity, Algorithm vs Pseudo code, Some Algorithm Types, Programming Languages, Python, Anaconda.
The document discusses lexical analysis in compilers. It describes how the lexical analyzer reads source code characters and divides them into tokens. Regular expressions are used to specify patterns for token recognition. The lexical analyzer generates a finite state automaton to recognize these patterns. Lexical analysis is the first phase of compilation that separates the input into tokens for the parser.
Automata theory studies abstract computing devices and the types of tasks they are capable of. Alan Turing pioneered this field in the 1930s by studying Turing machines. The theory examines questions like which tasks can and cannot be performed by different models of machines. It also considers the distinction between what is computable versus the complexity of computation. Common concepts include finite automata, formal languages, and the Chomsky hierarchy for classifying language types. Proofs in automata theory involve techniques like deduction, induction, contradiction, and establishing results by definition.
This presentation educates you about Python - GUI Programming(Tkinter), Tkinter Programming with syntaxe example, Tkinter Widgets with Operator & Description, Standard attributes.
For more topics stay tuned with learnbay.
Yacc is a general tool for describing the input to computer programs. It generates a LALR parser that analyzes tokens from Lex and creates a syntax tree based on the grammar rules specified. Yacc was originally developed in the 1970s and generates C code for the syntax analyzer from a grammar similar to BNF. It has been used to build compilers for languages like C, Pascal, and APL as well as for other programs like document retrieval systems.
The document provides an overview of compilers by discussing:
1. Compilers translate source code into executable target code by going through several phases including lexical analysis, syntax analysis, semantic analysis, code optimization, and code generation.
2. An interpreter directly executes source code statement by statement while a compiler produces target code as translation. Compiled code generally runs faster than interpreted code.
3. The phases of a compiler include a front end that analyzes the source code and produces intermediate code, and a back end that optimizes and generates the target code.
Lex is a program generator designed for lexical processing of character input streams. It works by translating a table of regular expressions and corresponding program fragments provided by the user into a program. This program then reads an input stream, partitions it into strings matching the given expressions, and executes the associated program fragments in order. Flex is a fast lexical analyzer generator that is an alternative to Lex. It generates scanners that recognize lexical patterns in text based on pairs of regular expressions and C code provided by the user.
The document provides information about shells in Linux operating systems. It defines what a kernel and shell are, explains why shells are used, describes different types of shells, and provides examples of shell scripting. The key points are:
- The kernel manages system resources and acts as an intermediary between hardware and software. A shell is a program that takes commands and runs them, providing an interface between the user and operating system.
- Shells are useful for automating tasks, combining commands to create new ones, and adding functionality to the operating system. Common shells include Bash, Bourne, C, Korn, and Tcsh.
- Shell scripts allow storing commands in files to automate tasks.
The document discusses recursion through examples such as calculating factorials and multiplying numbers recursively. It defines recursion as solving a problem by solving smaller instances of the same problem. Recursive algorithms break down a problem into smaller sub-problems until reaching a base case, and use the solutions to the sub-problems to solve the original problem.
The document discusses the Lisp programming language. It notes that Allegro Common Lisp will be used and lists textbooks for learning Lisp. It provides 10 points on Lisp, including that it is interactive, dynamic, uses symbols and lists as basic data types, prefix notation for operators, and classifies different data types. Evaluation follows simple rules and programs can be treated as both instructions and data.
This document discusses assembly language and assemblers. It begins by explaining that assembly language provides a more readable and convenient way to program compared to machine language. It then describes how an assembler works, translating assembly language programs into machine code. The elements of assembly language are defined, including mnemonic operation codes, symbolic operands, and data declarations. The document also covers instruction formats, sample assembly language programs, and the processing an assembler performs to generate machine code from assembly code.
This document discusses algorithms and their analysis. It defines an algorithm as a step-by-step procedure to solve a problem or calculate a quantity. Algorithm analysis involves evaluating memory usage and time complexity. Asymptotics, such as Big-O notation, are used to formalize the growth rates of algorithms. Common sorting algorithms like insertion sort and quicksort are analyzed using recurrence relations to determine their time complexities as O(n^2) and O(nlogn), respectively.
In this PPT you will learn how to use looping in python.
For more presentation in any subject please contact us on
[email protected].
You get a new presentation every Sunday at 10 AM.
Learn more about Python by clicking on given below link
Python Introduction- https://www.slideshare.net/RaginiJain21/final-presentation-on-python
Basic concept of Python -https://www.slideshare.net/RaginiJain21/python-second-ppt
Python Datatypes - https://www.slideshare.net/RaginiJain21/data-types-in-python-248466302
Python Library & Module - https://www.slideshare.net/RaginiJain21/python-libraries-and-modules
Basic Python Programs- https://www.slideshare.net/RaginiJain21/basic-python-programs
Python Media Libarary - https://www.slideshare.net/RaginiJain21/python-media-library
Recursion is defined as a technique where a function calls itself, either directly or indirectly. It involves breaking a problem down into smaller sub-problems until it reaches a base case. For recursion to work properly, it needs a base case where the problem can be solved without further recursion, and each recursive call must make progress towards the base case. While recursion can provide elegant solutions, it uses more memory and time than iterative approaches like loops. Common pitfalls are not having a base case, not making progress on each call, or using too many resources on each recursive call.
Regular expressions are a powerful tool for searching, matching, and parsing text patterns. They allow complex text patterns to be matched with a standardized syntax. All modern programming languages include regular expression libraries. Regular expressions can be used to search strings, replace parts of strings, split strings, and find all occurrences of a pattern in a string. They are useful for tasks like validating formats, parsing text, and finding/replacing text. This document provides examples of common regular expression patterns and methods for using regular expressions in Python.
This document discusses language processors and their fundamentals. It begins by explaining the semantic gap between how software is designed and implemented, and how language processors help bridge this gap. It then covers different types of language processors like translators, interpreters, and preprocessors. The key activities of language processors - analysis and synthesis - are explained. Analysis includes lexical, syntax and semantic analysis, while synthesis includes memory allocation and code generation. Language specifications using grammars and different binding times are also covered. Finally, common language processing development tools like LEX and YACC are introduced.
Pseudocode is an outline of a program written in a way that can be easily converted to a programming language. It uses natural language and programming constructs like expressions, methods, decision structures, loops, and array indexing. The three essential programming constructs are selection (if/else), repetition (while loops), and sequence. Pseudocode also uses relational, logical, and arithmetic operators and follows rules like using meaningful names and indenting code blocks.
This presentation contains:
1. Introduction
2. Central areas of TOC
3. Complexity theory
4. Computability theory
5. Automata theory
6. Related terminologies
7. Strings
8. Languages
9. Proof, Theorem, Lemma, Corollaries
The document discusses recursive functions and provides examples of recursive algorithms for calculating factorial, greatest common divisor (GCD), Fibonacci numbers, power functions, and solving the Towers of Hanoi problem. Recursive functions are functions that call themselves during their execution. They break down problems into subproblems of the same type until reaching a base case. This recursive breakdown allows problems to be solved in a top-down, step-by-step manner.
This document provides an overview of the Lisp programming language. It discusses key features of Lisp including its invention in 1958, machine independence, dynamic updating, and wide data types. The document also covers Lisp syntax, data types, variables, constants, operators, decision making, arrays, loops, text editors, and common uses of Lisp like Emacs. Overall, the document serves as a high-level introduction to the concepts and capabilities of the Lisp programming language.
This document provides an overview of the LISP programming language. It discusses LISP syntax, data types, functions, predicates, conditionals, variables, and other core concepts. Some key points:
- LISP uses prefix notation and parentheses extensively. Functions are applied using (func arg1 arg2).
- Core data types include numbers, symbols, strings, and lists. Lists are a fundamental data structure and there are many functions for manipulating them.
- Functions are defined using defun and can take parameters. Predicates test conditions and return t or nil.
- Conditionals include cond, if, when, and case. Assignment is done with setq.
-
Here is a recursive function to check if a list contains an element:
(defun contains (element list)
(cond ((null list) nil)
((equal element (car list)) t)
(t (contains element (cdr list)))))
To check the guest list:
(contains 'robocop guest-list)
This function:
1. Base case: If list is empty, element is not contained - return nil
2. Check if element equals car of list - if so, return t
3. Otherwise, recursively call contains on element and cdr of list
So it will recursively traverse the list until it finds a match or reaches empty list.
This document provides a brief introduction to the Lisp programming language. It discusses Lisp's history from its origins in 1958 to modern implementations like Common Lisp and Scheme. It also covers Lisp's support for functional, imperative, and object-oriented paradigms. A key feature of Lisp is its use of s-expressions as both code and data, which enables powerful macros to transform and generate code at compile time.
This document provides an overview of the LISP (List Processing) programming language. It discusses how LISP was commonly used for artificial intelligence programming due to its ability to modify programs dynamically. The document then describes various LISP dialects and the invention of LISP by John McCarthy. It also summarizes key LISP features like being machine-independent, providing object-oriented programming and advanced data types. The document concludes by explaining functions, predicates, conditionals, recursion, arrays, property lists, mapping functions and lambda expressions in LISP.
Lisp was invented in 1958 by John McCarthy and was one of the earliest high-level programming languages. It has a distinctive prefix notation and uses s-expressions to represent code as nested lists. Lisp features include built-in support for lists, dynamic typing, and an interactive development environment. It has been widely used in artificial intelligence and remains a popular language for prototyping due to its simple syntax and ability to treat code as data.
This document provides an introduction to the Lisp programming language. It discusses the history of Lisp, which was created in 1958. It also covers key Lisp concepts like S-expressions, atoms, function definition, evaluation, and macros. Macros allow programmers to generate Lisp code from Lisp code, extending the language. The document uses examples to demonstrate Lisp evaluation and features like conditional evaluation, higher-order functions, and special forms like 'quote and 'if.
Expressions, evaluation, and assignments are fundamental concepts in programming languages. Expressions specify computations and are evaluated based on operator precedence, associativity, and operand evaluation order. Assignment statements assign values to variables and can be used as expressions themselves. Different languages take different approaches to type conversions, side effects, and mixed-mode assignments, balancing flexibility, error detection, and optimization.
LISP is a programming language invented in 1958 that uses two simple data structures - atoms and lists. It heavily relies on recursion and functional programming. LISP defines all data as lists and represents programs as nested function calls, allowing for dynamic typing and easy abstraction. It introduced many concepts still used in modern languages, including conditionals, recursion, dynamic typing, garbage collection, and representing programs as mathematical expressions.
LISP Language, LISP Introduction, List Processing, LISP Syntax, Lisp Comparison Structures, Lisp Applications. Using of LISP language in Artificial Intelligence
Lisp and Scheme are dialects of the Lisp programming language. Scheme is favored for teaching due to its simplicity. Key features of Lisp include S-expressions as the universal data type, functional programming style with first-class functions, and uniform representation of code and data. Functions are defined using DEFINE and special forms like IF and COND control program flow. Common list operations include functions like CAR, CDR, CONS, LIST, and REVERSE.
This document discusses functional programming concepts. Functional programming focuses on using mathematical functions and immutable data. Popular functional languages include Lisp, Python, Erlang, and Haskell. Functional programming is well-suited for symbolic computation and list processing applications as it is based on mathematical concepts. Functions in functional programming have no side effects and treat all variables as immutable.
Nikolay Mozgovoy is a developer, mentor, and teacher who has worked with Sigma Software since 2013. He is also a prizewinner and organizer for Global Game Jam Ukraine. This document discusses the history and innovations of the Lisp programming language, which was created in 1960. It highlights Lisp's features like recursion, functions as first-class citizens, homoiconic syntax, and metaprogramming abilities. The primary Lisp dialects today are Scheme, Common Lisp, and Clojure.
The document provides an overview of functional programming, including its key features, history, differences from imperative programming, and examples using Lisp and Scheme. Some of the main points covered include:
- Functional programming is based on evaluating mathematical functions rather than modifying state through assignments.
- It uses recursion instead of loops and treats functions as first-class objects.
- Lisp was the first functional language in 1960 and introduced many core concepts like lists and first-class functions. Scheme was developed in 1975 as a simpler dialect of Lisp.
- Functional programs are more focused on what to compute rather than how to compute it, making them more modular and easier to reason about mathematically.
This document contains lecture slides on programming with LISP control structures from a CSE240 - Introduction to Programming Languages course. It discusses LISP data types and expressions, functions, conditionals with IF and PROGN, loops with DOTIMES, variable scope using LET, and global and local variables. It provides examples of each concept to demonstrate LISP syntax and evaluation.
C supports various arithmetic operators to perform calculations on integer, float, and double data types. Expressions in C are made up of operands and operators, and follow specific precedence rules when evaluating complex expressions. Parentheses can be used to alter the default order of operations. Functions like increment/decrement can be applied either before or after using the operand in an expression.
This document discusses principles of sequence control in programming languages. It covers expressions, assignment statements, selection statements like if/else and switch/case, and iterative statements like for, while, and loops controlled by data structures. It provides examples of how these concepts are implemented in different languages like C, Pascal, and C#. Unconditional branching with goto is also discussed, noting that while powerful it can hurt readability so most modern languages avoid or restrict it.
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AWS: Messaging Services in AWS PresentationKnoldus Inc.
Asynchronous messaging allows services to communicate by sending and receiving messages via a queue. This enables services to remain loosely coupled and promote service discovery. To implement each of these message types, AWS offers various managed services such as Amazon SQS, Amazon SNS, Amazon EventBridge, Amazon MQ, and Amazon MSK. These services have unique features tailored to specific needs.
Amazon Cognito: A Primer on Authentication and AuthorizationKnoldus Inc.
Amazon Cognito is a service provided by Amazon Web Services (AWS) that facilitates user identity and access management in the cloud. It's commonly used for building secure and scalable authentication and authorization systems for web and mobile applications.
ZIO Http A Functional Approach to Scalable and Type-Safe Web DevelopmentKnoldus Inc.
Explore the transformative power of ZIO HTTP - a powerful, purely functional library designed for building highly scalable, concurrent and type-safe HTTP service. Delve into seamless integration of ZIO's powerful features offering a robust foundation for building composable and immutable web applications.
Managing State & HTTP Requests In Ionic.Knoldus Inc.
Ionic is a complete open-source SDK for hybrid mobile app development created by Max Lynch, Ben Sperry, and Adam Bradley of Drifty Co. in 2013.The original version was released in 2013 and built on top of AngularJS and Apache Cordova. However, the latest release was re-built as a set of Web Components using StencilJS, allowing the user to choose any user interface framework, such as Angular, React or Vue.js. It also allows the use of Ionic components with no user interface framework at all.[4] Ionic provides tools and services for developing hybrid mobile, desktop, and progressive web apps based on modern web development technologies and practices, using Web technologies like CSS, HTML5, and Sass. In particular, mobile apps can be built with these Web technologies and then distributed through native app stores to be installed on devices by utilizing Cordova or Capacitor.
Agentic Techniques in Retrieval-Augmented Generation with Azure AI SearchMaxim Salnikov
Discover how Agentic Retrieval in Azure AI Search takes Retrieval-Augmented Generation (RAG) to the next level by intelligently breaking down complex queries, leveraging full conversation history, and executing parallel searches through a new LLM-powered query planner. This session introduces a cutting-edge approach that delivers significantly more accurate, relevant, and grounded answers—unlocking new capabilities for building smarter, more responsive generative AI applications.
Traditional Retrieval-Augmented Generation (RAG) pipelines work well for simple queries—but when users ask complex, multi-part questions or refer to previous conversation history, they often fall short. That’s where Agentic Retrieval comes in: a game-changing advancement in Azure AI Search that brings LLM-powered reasoning directly into the retrieval layer.
This session unveils how agentic techniques elevate your RAG-based applications by introducing intelligent query planning, subquery decomposition, parallel execution, and result merging—all orchestrated by a new Knowledge Agent. You’ll learn how this approach significantly boosts relevance, groundedness, and answer quality, especially for sophisticated enterprise use cases.
Key takeaways:
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- Get hands-on guidance on integrating Agentic Retrieval with Azure AI Foundry and SDKs
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Whether you're building intelligent copilots, enterprise Q&A bots, or AI-driven search solutions, this session will equip you with the tools and patterns to push beyond traditional RAG.
Women in Tech: Marketo Engage User Group - June 2025 - AJO with AWSBradBedford3
Creating meaningful, real-time engagement across channels is essential to building lasting business relationships. Discover how AWS, in collaboration with Deloitte, set up one of Adobe's first instances of Journey Optimizer B2B Edition to revolutionize customer journeys for B2B audiences.
This session will share the use cases the AWS team has the implemented leveraging Adobe's Journey Optimizer B2B alongside Marketo Engage and Real-Time CDP B2B to deliver unified, personalized experiences and drive impactful engagement.
They will discuss how they are positioning AJO B2B in their marketing strategy and how AWS is imagining AJO B2B and Marketo will continue to work together in the future.
Whether you’re looking to enhance customer journeys or scale your B2B marketing efforts, you’ll leave with a clear view of what can be achieved to help transform your own approach.
Speakers:
Britney Young Senior Technical Product Manager, AWS
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1. Integration Events: Shift from synchronous calls to pre-fetching to reduce query latency and improve user experience.
2. Task Queue: Offload non-critical tasks like notifications to streamline request flows.
3. Task Scheduler: Enable precise, fault-tolerant delayed or recurring workflows with robust scheduling.
4. Iterator for Long-running Jobs: Process extensive workloads via chunked execution, optimizing scalability and resilience.
For each pattern, we’ll discuss benefits, challenges, and how we mitigate drawbacks to create practical solutions
This session offers actionable insights for developers and architects tackling distributed systems, helping refine microservices and adopting Kafka-driven async excellence.
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Explore innovative tools tailored for modern finance with our Money Lender Software Development, efficient Daily Pigmy Collection Software, and streamlined Personal Loan Software. This presentation showcases how these solutions simplify loan management, boost collection efficiency, and enhance customer experience for NBFCs, microfinance firms, and individual lenders.
Artificial Intelligence Applications Across IndustriesSandeepKS52
Artificial Intelligence is a rapidly growing field that influences many aspects of modern life, including transportation, healthcare, and finance. Understanding the basics of AI provides insight into how machines can learn and make decisions, which is essential for grasping its applications in various industries. In the automotive sector, AI enhances vehicle safety and efficiency through advanced technologies like self-driving systems and predictive maintenance. Similarly, in healthcare, AI plays a crucial role in diagnosing diseases and personalizing treatment plans, while in financial services, it helps in fraud detection and risk management. By exploring these themes, a clearer picture of AI's transformative impact on society emerges, highlighting both its potential benefits and challenges.
Generative Artificial Intelligence and its ApplicationsSandeepKS52
The exploration of generative AI begins with an overview of its fundamental concepts, highlighting how these technologies create new content and ideas by learning from existing data. Following this, the focus shifts to the processes involved in training and fine-tuning models, which are essential for enhancing their performance and ensuring they meet specific needs. Finally, the importance of responsible AI practices is emphasized, addressing ethical considerations and the impact of AI on society, which are crucial for developing systems that are not only effective but also beneficial and fair.
Have you upgraded your application from Qt 5 to Qt 6? If so, your QML modules might still be stuck in the old Qt 5 style—technically compatible, but far from optimal. Qt 6 introduces a modernized approach to QML modules that offers better integration with CMake, enhanced maintainability, and significant productivity gains.
In this webinar, we’ll walk you through the benefits of adopting Qt 6 style QML modules and show you how to make the transition. You'll learn how to leverage the new module system to reduce boilerplate, simplify builds, and modernize your application architecture. Whether you're planning a full migration or just exploring what's new, this session will help you get the most out of your move to Qt 6.
INTRODUCTION:TRANSMISSION MEDIA
• A transmission media in data communication is a physical path between the sender and
the receiver and it is the channel through which data can be sent from one location to
another. Data can be represented through signals by computers and other sorts of
telecommunication devices. These are transmitted from one device to another in the
form of electromagnetic signals. These Electromagnetic signals can move from one
sender to another receiver through a vacuum, air, or other transmission media.
Electromagnetic energy mainly includes radio waves, visible light, UV light, and gamma
ra
Looking for a BIRT Report Alternative Here’s Why Helical Insight Stands Out.pdfVarsha Nayak
The search for an Alternative to BIRT Reports has intensified as companies face challenges with BIRT's steep learning curve, limited visualization capabilities, and complex deployment requirements. Organizations need reporting solutions that offer intuitive design interfaces, comprehensive analytics features, and seamless integration capabilities – all while maintaining the reliability and performance that enterprise environments demand.
Integrating Survey123 and R&H Data Using FMESafe Software
West Virginia Department of Transportation (WVDOT) actively engages in several field data collection initiatives using Collector and Survey 123. A critical component for effective asset management and enhanced analytical capabilities is the integration of Geographic Information System (GIS) data with Linear Referencing System (LRS) data. Currently, RouteID and Measures are not captured in Survey 123. However, we can bridge this gap through FME Flow automation. When a survey is submitted through Survey 123 for ArcGIS Portal (10.8.1), it triggers FME Flow automation. This process uses a customized workbench that interacts with a modified version of Esri's Geometry to Measure API. The result is a JSON response that includes RouteID and Measures, which are then applied to the feature service record.
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Advanced Token Development - Decentralized Innovationarohisinghas720
The world of blockchain is evolving at a fast pace, and at the heart of this transformation lies advanced token development. No longer limited to simple digital assets, today’s tokens are programmable, dynamic, and play a crucial role in driving decentralized applications across finance, governance, gaming, and beyond.
Automated Migration of ESRI Geodatabases Using XML Control Files and FMESafe Software
Efficient data migration is a critical challenge in geospatial data management, especially when working with complex data structures. This presentation explores an automated approach to migrating ESRI Geodatabases using FME and XML-based control files. A key advantage of this method is its adaptability: changes to the data model are seamlessly incorporated into the migration process without requiring modifications to the underlying FME workflow. By separating data model definitions from migration logic, this approach ensures flexibility, reduces maintenance effort, and enhances scalability.
2. Agenda
● Why LISP
● What is LISP
● Timeline of Lisp dialects
● Fundamentals of LISP
● Expression Evaluation in LISP
● Defining Variables
● Evaluating Combinations
● Procedure Definitions
● The Substitution Model for Procedure Application
● Conditional Expressions
● Linear Recursion and Iteration
3. Why LISP
The Key motivations behind Learning LISP are as follow:-
● Some of the features of Scala are inherited from LISP.
● Scala is Hybrid Programming Language. It consist features of both
Functional and Object Oriented Programming Language.
● We most of us know the Object Oriented programming Language like JAVA,
C++ etc.
● Undersanding the Basics of LISP can help us understand the other
functional programming languages like clojure.
4. What is LISP
● LISP, an acronym for list processing, is a programming language that was
designed for easy manipulation of data strings.
● Developed in 1959 by John McCarthy,
● It is a commonly used language for artificial intelligence (AI) programming.
● It is one of the oldest programming languages still in relatively wide use.
● All program code is written as s-expressions, or parenthesized lists.
6. What is LISP
● LISP, an acronym for list processing, is a programming language that was
designed for easy manipulation of data strings.
● Developed in 1959 by John McCarthy,
● It is a commonly used language for artificial intelligence (AI) programming.
● It is one of the oldest programming languages still in relatively wide use.
● All program code is written as s-expressions, or parenthesized lists.
7. Fundamentals of LISP
Every powerful language has three mechanisms for accomplishing this :-
● Primitive expressions, which represent the simplest entities the language
is concerned with,
● Means of combination, by which compound elements are built from
simpler ones, and
● Means of abstraction, by which compound elements can be named and
manipulated as units.
8. Expression Evaluation in LISP
Expressions representing numbers may be combined with an expression representing a
primitive procedure (such as + or *) to form a compound expression that represents the
application of the procedure to those numbers. For example:-
(+ 137 349)
486
(- 1000 334)
666
(* 5 99)
495
(/ 10 5)
2
9. Expression Evaluation in LISP
Expressions such as these, formed by delimiting a list of expressions within
parentheses in order to denote procedure application, are called combinations.
● The leftmost element in the list is called the operator, and the other elements are
called operands.
● The value of a combination is obtained by applying the procedure specified by the
operator to the arguments that are the values of the operands.
● The convention of placing the operator to the left of the operands is known as
prefix notation.
● No ambiguity can arise, because the operator is always the leftmost element and
the entire combination is delimited by the parentheses.
10. Defining Variables
Define is LISP language's simplest means of abstraction, for it allows us to
use simple names to refer to the results of compound operation.
(define pi 3.14159)
(define radius 10)
(* pi (* radius radius))
314.159
(define circumference (* 2 pi radius))
Circumference
62.8318
11. Evaluating Combinations
To evaluate a combination, do the following:
● 1. Evaluate the subexpressions of the combination.
● 2. Apply the procedure that is the
value of the leftmost subexpression
(the operator) to the arguments
that are the values of the other
subexpressions (the operands).
(* (+ 2 (* 4 6))
(+ 3 5 7))
12. Evaluating Combinations
Each combination is represented by a node with branches corresponding to the
operator and the operands of the combination stemming from it.
The terminal nodes (that is, nodes with no branches stemming from them)
represent either operators or numbers. Viewing evaluation in terms of the tree,
we can imagine that the values of the operands percolate upward, starting from
the terminal nodes and then combining at higher and higher levels.
the ``percolate values upward'' form of the evaluation rule is an example of a
general kind of process known as tree accumulation.
13. Procedure Definitions
A much more powerful abstraction technique by which a compound operation can
be given a name and then referred to as a unit.
(define (square x) (* x x))
We can understand this in the following way:
(define (square x) (* x x))
square something, multiply it by itself.
We have here a compound procedure, which has been given the name square. The
procedure represents the operation of multiplying something by itself. The thing to
be multiplied is given a local name, x, which plays the same role that a pronoun
plays in natural language. Evaluating the definition creates this compound
procedure and associates it with the name square
14. The Substitution Model for Procedure Application
There Exists 2 type of Orders to Evaluate any Procedure in LISP.
● Normal Order
● Applicative Order
● Normal Order :- the interpreter first evaluates the operator and operands
and then applies the resulting procedure to the resulting arguments
15. Normal Order
Normal Order Example :-
(sum-of-squares (+ 5 1) (* 5 2))
(+ (square 6) (square 10 ) )
If we use the definition of square, this reduces to
(+ (* 6 6) (* 10 10))
which reduces by multiplication to
(+ 36 100)
and finally to
136
16. Applicative Order
● Applicative Order :- An alternative evaluation model would not evaluate the operands until their
values were needed. Instead it would first substitute operand expressions for parameters until it
obtained an expression involving only primitive operators, and would then perform the evaluation.
(sum-of-squares (+ 5 1) (* 5 2))
(+ (square (+ 5 1)) (square (* 5 2)) )
(+ (* (+ 5 1) (+ 5 1)) (* (* 5 2) (* 5 2)))
followed by the reductions
(+ (* 6 6) (* 10 10))
(+ 36 100)
136
17. Conditional Expressions
Most of the time situation arises when we need to perform some operation based on
some condition.
For Example :- Finding the Absolute value.
Condition will be :-
● IF x > 0 return x
IF x = 0 return 0
IF x < 0 return -x
This construct is called a case analysis, and there is a special form in Lisp for notating such
a case analysis. It is called cond (which stands for ‘‘conditional’’), and it is used as follows:
● (define (abs x)
(cond ((> x 0) x)
((= x 0) 0)
((< x 0) (- x))))
18. Linear Recursion and Iteration
● Recursion:- the repeated application of a recursive procedure or definition.
● Iteration:- the repetition of a process.
Example:- Finding the Factorial of some number.This can easily implemented in 2
ways .
● Using Recusrion
● Using Iteration.
19. Linear Recursion
● Factorial Using the Recursion in LISP
(define (factorial n)
(if (= n 1)
1
(* n (factorial (- n 1)))))
● Here, Time Complexity is O(x)
● Space Complexity is O(x)