Deep dive into algorithms and algorithmic complexity
Understand the key metrics for evaluating an algorithm
Understand the software development life cycle and how to efficiently develop optimal solutions
The document provides an overview of the Python programming language. It discusses what Python is, its history and origins, why it is popular, common applications, and who uses it. It then covers running Python, variables, data types, input/output functions, conditional and looping statements. Specific Python concepts explained in more detail include variables, common data types (numeric, string, list, tuple, dictionary, Boolean), functions for lists and tuples, and if/else and for/while loop statements. The document is intended as an introductory guide to Python.
Introduction to Structure Programming with C++Mohamed Essam
Functions allow code to be reused by grouping a series of statements together to perform a specific task. A function definition specifies the data types of parameters and return values, and the function body contains the statements. Functions are called by their name followed by parentheses, and actual arguments are passed inside the parentheses. Parameters act as variables that represent the arguments passed into the function.
This document provides an agenda and overview for a Python tutorial presented over multiple sessions. The first session introduces Python and demonstrates how to use the Python interpreter. The second session covers basic Python data structures like lists, modules, input/output, and exceptions. An optional third session discusses unit testing. The document explains that Python is an easy to learn yet powerful programming language that supports object-oriented programming and high-level data structures in an interpreted, dynamic environment.
In this PPT you learn some basic terminology and basic concept of Python which is a pillar of python programming.So learn Python programming by these PPT.
You get a new presentation every Sunday at 10 AM.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
Introduction to Python programming LanguageMansiSuthar3
Python is a popular, high-level programming language that is used for a variety of tasks including web development, machine learning, and data science. It has a simple syntax and is readable. Python has built-in data types like integers, floats, booleans, strings, lists, tuples, and dictionaries. It also supports object-oriented programming. Common operations in Python include conditional statements, loops, functions, packages, file handling, classes, and data visualization using libraries like NumPy, Matplotlib, and Seaborn.
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.
The document provides an overview of the Python programming language. It discusses what Python is, its history and origins, why it is popular, common applications, and who uses it. It then covers running Python, variables, data types, input/output functions, conditional and looping statements. Specific Python concepts explained in more detail include variables, common data types (numeric, string, list, tuple, dictionary, Boolean), functions for lists and tuples, and if/else and for/while loop statements. The document is intended as an introductory guide to Python.
Introduction to Structure Programming with C++Mohamed Essam
Functions allow code to be reused by grouping a series of statements together to perform a specific task. A function definition specifies the data types of parameters and return values, and the function body contains the statements. Functions are called by their name followed by parentheses, and actual arguments are passed inside the parentheses. Parameters act as variables that represent the arguments passed into the function.
This document provides an agenda and overview for a Python tutorial presented over multiple sessions. The first session introduces Python and demonstrates how to use the Python interpreter. The second session covers basic Python data structures like lists, modules, input/output, and exceptions. An optional third session discusses unit testing. The document explains that Python is an easy to learn yet powerful programming language that supports object-oriented programming and high-level data structures in an interpreted, dynamic environment.
In this PPT you learn some basic terminology and basic concept of Python which is a pillar of python programming.So learn Python programming by these PPT.
You get a new presentation every Sunday at 10 AM.
Introduction To Programming with PythonSushant Mane
The document provides an introduction to the Python programming language. It discusses Python's core features like being an interpreted, object-oriented, and dynamic language. It covers basic Python concepts like data types, variables, operators, control flow, functions, modules, file handling, and object-oriented programming. The document contains examples and explanations of built-in types like numbers, strings, lists, tuples, and dictionaries. It also discusses control structures, functions, modules, and classes in Python.
Introduction to Python programming LanguageMansiSuthar3
Python is a popular, high-level programming language that is used for a variety of tasks including web development, machine learning, and data science. It has a simple syntax and is readable. Python has built-in data types like integers, floats, booleans, strings, lists, tuples, and dictionaries. It also supports object-oriented programming. Common operations in Python include conditional statements, loops, functions, packages, file handling, classes, and data visualization using libraries like NumPy, Matplotlib, and Seaborn.
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.
Learn from LL(1) to PEG parser the hard wayKir Chou
The document discusses parsers in Python and the motivation for changing CPython's parser from LL(1) to PEG. It covers parser concepts like CFG, LL(1) parsers, LR(0) parsers, PEG grammars, and Packrat parsing. The talk explains how CPython now uses a PEG parser generator to build the parser from a grammar, avoiding the intermediate CST construction of the previous LL(1) parser. Benefits of the PEG parser include more flexible grammars with infinite lookahead and skipping the CST generation step.
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
This document provides a summary of MATLAB commands and their equivalents in numerical Python (NumPy) and R. It covers topics such as help, operators, vectors, matrices, random number generation and more. The goal is to provide a quick reference for switching between MATLAB, Python, Scilab, Octave, Gnuplot and R for numeric processing and data visualization. Equivalents are given for each MATLAB command or function in the other languages.
Python If Else | If Else Statement In Python | EdurekaEdureka!
YouTube Link: https://youtu.be/nMEFZ6TvkDA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'If Else In Python' will help you understand how you can use a conditional if and else statements in python for decision making with concepts like shorthand if and else, nested if-else, etc. Following are the topics discussed:
What Are Python Conditions?
What Is If And If Else In Python?
Syntax For If Else In Python
Shorthand If Else
Use Case - Nested If Else
Follow us to never miss an update in the future.
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Erlang was invented in 1986 by Joe Armstrong for building fault-tolerant telecom systems at Ericsson. It achieved 99.9% uptime in these systems. Some key reasons to use Erlang include its ability to easily write concurrent and distributed programs, build highly available systems, and take advantage of multicore processors. Popular applications built with Erlang include WhatsApp, RabbitMQ, Riak, and Ejabberd. The document then provides examples of basic Erlang concepts like arithmetic, variables, atoms, tuples, lists, strings, and pattern matching. It also shows how to compile and run a simple "Hello World" program.
Slides for Lecture 3 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
Strings useful string operations.
Lectures from a Python workshop I taught in 2013 at the University of Pittsburgh. These are introductory slides to teach important aspects of the Python language.
Update: python limits the number of recursion calls, so computing the factorial as shown in the slides may not be generally usable.
This document provides an introduction to LR parsing and bottom-up parsing. It discusses that LR(1) parsing is a bottom-up parsing technique that uses a parser stack, parsing table, and driver function. It examines the bottom-up parser actions of shift, reduce, accept, and error. Examples are provided to illustrate bottom-up parsing and how it reduces the input string to the starting symbol using the reverse of production rules. Potential parsing issues are also discussed, such as how the parser chooses between shifting and reducing and handling situations with multiple reduction choices.
This document provides an introduction to the Python programming language. It discusses what Python is, what it can be used for, its syntax compared to other languages, how to get started with Python, variables, data types, numbers, and random numbers. Key points include that Python is an interpreted, multi-paradigm programming language used for web development, software development, mathematics, and more. It uses indentation rather than curly brackets and has a simple syntax. Variables do not require declaration, and Python has built-in data types like integers, floats, strings, lists, dictionaries, and more.
This document provides an introduction and overview of the Python programming language. It discusses Python's key features such as being an interpreted, object-oriented, high-level programming language with dynamic typing and a large standard library. It also covers Python's use as both a scripting and general purpose language. The document then discusses Python's data types, operators, control flow statements, functions, and lambda expressions. It provides examples of using Python interactively and in script mode.
This document discusses top-down parsing and LL parsers. It provides an introduction to top-down parsing and describes how it works by hypothesizing general parse tree structures and checking compatibility with known structures. It also discusses LL parsers and how they use a top-down parsing strategy. Finally, it covers recursive descent parsing, predictive parsing, building a predictive parsing table using FIRST and FOLLOW functions, and how a non-recursive predictive parser works by consulting a parsing table.
This document provides an introduction to the Python programming language. It discusses Python language elements like literals, keywords, identifiers, operators, expressions, statements, and comments. It also covers Python data types, variables, printing values, and how to write and run a simple Python program. The document aims to explain Python concepts in a step-by-step manner to help readers learn the basics of the Python language.
1. The lesson plan covers an introduction to Python programming using different loops and conditional statements. It includes a warm up, presentation, video, practical work in pairs or online, questions and answers, reflection, and homework.
2. Students learn about while loops, for loops, logical and relational operators, if/else statements, and functions through demonstrations, examples, and hands-on coding exercises.
3. The homework assignment involves designing a flowchart using Arduino to control an electric circuit and save wasted energy by applying loops, conditional statements, and a function with parameters.
Effective Algorithm for n Fibonacci Number By: Professor Lili SaghafiProfessor Lili Saghafi
Understand the definition of the Fibonacci numbers.
Understand the definition of the Recursive / Recursive Functions
Show that the naive algorithm for computing them is slow.
Efficiently create algorithms to compute large Fibonacci numbers.
The right algorithm makes all the difference.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
This document discusses top-down parsing and predictive parsing. It begins with an overview of top-down parsing, noting that the parse tree is constructed from the top-down and left-to-right. It then discusses recursive descent parsing and its limitations for ambiguous, left-recursive, or non-left-factored grammars. Finally, it introduces LL(1) parsing and how LL(1) parsing tables can be used to predict the next production without backtracking, making parsing more efficient.
The document provides information about getting help with algorithm assignments. It lists a website, email address, and phone number that can be used for support regarding algorithm homework help.
Learn from LL(1) to PEG parser the hard wayKir Chou
The document discusses parsers in Python and the motivation for changing CPython's parser from LL(1) to PEG. It covers parser concepts like CFG, LL(1) parsers, LR(0) parsers, PEG grammars, and Packrat parsing. The talk explains how CPython now uses a PEG parser generator to build the parser from a grammar, avoiding the intermediate CST construction of the previous LL(1) parser. Benefits of the PEG parser include more flexible grammars with infinite lookahead and skipping the CST generation step.
After the end of lesson you will be able to learn Python basics-What Python is? Its releases. Where we can use Python? Python Features. Tokens, comments variables etc... In out next PPT you will learn how to input and get output in Python
This document provides a summary of MATLAB commands and their equivalents in numerical Python (NumPy) and R. It covers topics such as help, operators, vectors, matrices, random number generation and more. The goal is to provide a quick reference for switching between MATLAB, Python, Scilab, Octave, Gnuplot and R for numeric processing and data visualization. Equivalents are given for each MATLAB command or function in the other languages.
Python If Else | If Else Statement In Python | EdurekaEdureka!
YouTube Link: https://youtu.be/nMEFZ6TvkDA
** Python Certification Training: https://www.edureka.co/python **
This Edureka PPT on 'If Else In Python' will help you understand how you can use a conditional if and else statements in python for decision making with concepts like shorthand if and else, nested if-else, etc. Following are the topics discussed:
What Are Python Conditions?
What Is If And If Else In Python?
Syntax For If Else In Python
Shorthand If Else
Use Case - Nested If Else
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
Castbox: https://castbox.fm/networks/505?country=in
Erlang was invented in 1986 by Joe Armstrong for building fault-tolerant telecom systems at Ericsson. It achieved 99.9% uptime in these systems. Some key reasons to use Erlang include its ability to easily write concurrent and distributed programs, build highly available systems, and take advantage of multicore processors. Popular applications built with Erlang include WhatsApp, RabbitMQ, Riak, and Ejabberd. The document then provides examples of basic Erlang concepts like arithmetic, variables, atoms, tuples, lists, strings, and pattern matching. It also shows how to compile and run a simple "Hello World" program.
Slides for Lecture 3 of the course: Introduction to Programming with Python offered at ICCBS.
It covers the following topics:
Strings useful string operations.
Lectures from a Python workshop I taught in 2013 at the University of Pittsburgh. These are introductory slides to teach important aspects of the Python language.
Update: python limits the number of recursion calls, so computing the factorial as shown in the slides may not be generally usable.
This document provides an introduction to LR parsing and bottom-up parsing. It discusses that LR(1) parsing is a bottom-up parsing technique that uses a parser stack, parsing table, and driver function. It examines the bottom-up parser actions of shift, reduce, accept, and error. Examples are provided to illustrate bottom-up parsing and how it reduces the input string to the starting symbol using the reverse of production rules. Potential parsing issues are also discussed, such as how the parser chooses between shifting and reducing and handling situations with multiple reduction choices.
This document provides an introduction to the Python programming language. It discusses what Python is, what it can be used for, its syntax compared to other languages, how to get started with Python, variables, data types, numbers, and random numbers. Key points include that Python is an interpreted, multi-paradigm programming language used for web development, software development, mathematics, and more. It uses indentation rather than curly brackets and has a simple syntax. Variables do not require declaration, and Python has built-in data types like integers, floats, strings, lists, dictionaries, and more.
This document provides an introduction and overview of the Python programming language. It discusses Python's key features such as being an interpreted, object-oriented, high-level programming language with dynamic typing and a large standard library. It also covers Python's use as both a scripting and general purpose language. The document then discusses Python's data types, operators, control flow statements, functions, and lambda expressions. It provides examples of using Python interactively and in script mode.
This document discusses top-down parsing and LL parsers. It provides an introduction to top-down parsing and describes how it works by hypothesizing general parse tree structures and checking compatibility with known structures. It also discusses LL parsers and how they use a top-down parsing strategy. Finally, it covers recursive descent parsing, predictive parsing, building a predictive parsing table using FIRST and FOLLOW functions, and how a non-recursive predictive parser works by consulting a parsing table.
This document provides an introduction to the Python programming language. It discusses Python language elements like literals, keywords, identifiers, operators, expressions, statements, and comments. It also covers Python data types, variables, printing values, and how to write and run a simple Python program. The document aims to explain Python concepts in a step-by-step manner to help readers learn the basics of the Python language.
1. The lesson plan covers an introduction to Python programming using different loops and conditional statements. It includes a warm up, presentation, video, practical work in pairs or online, questions and answers, reflection, and homework.
2. Students learn about while loops, for loops, logical and relational operators, if/else statements, and functions through demonstrations, examples, and hands-on coding exercises.
3. The homework assignment involves designing a flowchart using Arduino to control an electric circuit and save wasted energy by applying loops, conditional statements, and a function with parameters.
Effective Algorithm for n Fibonacci Number By: Professor Lili SaghafiProfessor Lili Saghafi
Understand the definition of the Fibonacci numbers.
Understand the definition of the Recursive / Recursive Functions
Show that the naive algorithm for computing them is slow.
Efficiently create algorithms to compute large Fibonacci numbers.
The right algorithm makes all the difference.
what is Parsing
different types of parsing
what is parser and role of parser
what is top-down parsing and bottom-up parsing
what is the problem in top-down parsing
design of top-down parsing and bottom-up parsing
examples of top-down parsing and bottom-up parsing
This document discusses top-down parsing and predictive parsing. It begins with an overview of top-down parsing, noting that the parse tree is constructed from the top-down and left-to-right. It then discusses recursive descent parsing and its limitations for ambiguous, left-recursive, or non-left-factored grammars. Finally, it introduces LL(1) parsing and how LL(1) parsing tables can be used to predict the next production without backtracking, making parsing more efficient.
The document provides information about getting help with algorithm assignments. It lists a website, email address, and phone number that can be used for support regarding algorithm homework help.
The document provides an overview of algorithms and pseudocode. It discusses key algorithm concepts like searching, sorting, and optimization problems. Specific searching algorithms covered are linear search and binary search, while sorting algorithms discussed include bubble sort and insertion sort. Pseudocode is introduced as a way to describe algorithms using plain language instead of a programming syntax. Examples are provided to demonstrate linear search, binary search, and bubble sort algorithms. The document aims to teach the fundamentals of algorithms through examples and explanations of common algorithm problems and solutions.
This document discusses algorithms and their analysis. It covers linear search, binary search, sorting algorithms, and analyzing time complexity using Big O, Big Theta, and Big Omega notation. Binary search runs in logarithmic time O(log n) while linear search takes linear time O(n). The traveling salesperson problem takes exponential time O(n!) and is considered an inefficient algorithm.
The document provides an introduction to algorithms and their analysis. It discusses the definition of an algorithm, their characteristics, types and examples. It also covers the analysis of algorithms including best, average and worst case analysis. Common asymptotic notations like Big-O, Omega and Theta notations are introduced to analyze the time complexity of algorithms. Examples of analyzing for loops and other control statements are provided.
The document discusses algorithms and their building blocks. It defines an algorithm as a collection of well-defined, unambiguous and effectively computable instructions that return a proper output. The three building blocks of algorithms are identified as sequence, selection, and iteration. Sequence refers to a series of actions performed in order. Selection allows a program to choose between different actions or conditions. Iteration allows a block of code to be repeated while a condition is true. Pseudocode and flowcharts are introduced as common ways to express algorithms and illustrate the control flow and logic of a program.
This document discusses algorithms and how computers use them. It begins with an introduction to algorithms, defining them as precise step-by-step instructions to complete a task. Examples of algorithms like making tea and sorting cards are provided. Two sorting algorithms are described: simple sort and selection sort. Tasks are included to have the student write algorithms and do card sorting. The document concludes by analyzing which sorting algorithm is most efficient based on comparisons and memory usage.
LESSON 3A. INTRODUCTION TO ITERATION: LOOPS, TRACE TABLES, WHILE LOOPS
Introduction to Iteration and loops. The theory behind loops and how they work. Create and adapt programs using loops. Intro to the random number generator. Learn about trace tabling (white box testing). Example of a trace table and dry run. Wonders of the Fibonacci sequence. Examples of Iteration in game design. Focus on While loops. Challenges, tasks (with solutions), suggested videos, big ideas discussion and research and HW included. Introducing Ada Lovelace and Charles Babbage.
The secret of Functional Programming revealed!Torbjørn Marø
Functional Programming (FP) uses functions rather than objects and avoids variables by using referential transparency. FP is more abstract but can express ideas with less code. It reduces bugs, is easier to test and verify, and promises easier parallel programming. The key to FP is separating programs into a functional part without side effects and an imperative part for interactions. Getting started involves using LINQ and learning FP languages.
This document provides an overview of for loops in Python programming. It discusses the anatomy of a for loop, using ranges to repeat code a set number of times, nested loops, the break statement, and examples of for loops in games and quizzes. It also provides background on the creator of Python programming language, Guido van Rossum, and discusses theories about the digital nature of the universe. Useful videos and additional reading links are included at the end.
A Gentle Introduction to Coding ... with PythonTariq Rashid
A gentle introduction to coding (programming) for complete beginners. Starting from then basics - electrical wires - proceeding through variables, data structures, loops, functions, and exploring libraries for visualisation and specialist tools. Finally we use flask to make a very simple twitter clone web application.
CMPS 5P Assignment 3 Spring 19Instructions1. The aim o.docxmary772
CMPS 5P Assignment 3 Spring 19
Instructions
1. The aim of this assignment is to practice Selection, Control Flows(Functions, Recursion,
break), Strings and use APIs in Python.
2. The deadline for the assignment is 05/06/2019 (Monday) 11:59 PM.
3. The grade of the assignment is out of 100 pts with 5 questions.
4. Students must use Python3 (NOT Python2) to write the code.
5. Don’t share code or get code from the internet, but working together is encouraged. If
you choose to work with someone you must mention them in one of the first comments
in the file.
6. Students are expected to write the code that is easily readable. To this end, they must
employ meaningful variable names and comment their code properly.
7. The output of the code requires to be precisely as shown in the sample runs.
8. Throughout the assignment ”” indicate strings, [] indicate lists and True/False indicates
bool. It is the student’s responsibility to resolve for themselves any ambiguity in the
assignment or instructions. Please use Piazza to get questions clarified.
9. Students must put the code in a single file named FirstName LastName StudentID HW3.py
and submit it through Canvas. DO NOT zip your submission. Zip is more likely used
when there is more than one file submission. Please notice that failing to com-
ply with this particular requirement will result in 0 points for the entire
assignment.
10. Each question has a function name already given. For the sake of ease in grading please
use the exact same name for your function. Inconsistent function names will be treated
as question unanswered which will fetch 0 points for that question.
11. It is important that you practice commenting your code, so each file submitted
without comments will have 5 points deducted from its score (even if the program
works perfectly)!
12. Each solution will be run against multiple test cases. The percentage of test cases
passed by your solution will factor into your score.
13. Use the skeleton code provided at Question 5 for your submission. You can start bottom
up by having this skeleton in place and then solving each of the individual questions
or top down by solving the individual questions first and then combining them to the
overall submission.
1
CMPS 5P Assignment 3 Spring 19
1 Position queries - 15 points
Write a function that is named positionQueries that takes 2 lists as input such that the
first list named data contains a list of unsorted integers and the second list named order
contains position queries. Order denotes the position of an element in the data list if it were
sorted. Thus, though the order elements can contain any value(as described below) valid
elements in order list cannot be less than 1 or greater than the length of the data list. The
output will be the list of integers in the data list corresponding to the order of elements. If
the input is bad print the message ”bad input” and return to solve the next question.
Examples:
Test Case 1:
input.
The document discusses Big O notation, which is used to classify algorithms based on how their running time scales with input size. It provides examples of common Big O notations like O(1), O(log n), O(n), O(n^2), and O(n!). The document also explains that Big O looks only at the fastest growing term as input size increases. Well-chosen data structures can help reduce an algorithm's Big O complexity. For example, searching a sorted list is O(log n) rather than O(n) for an unsorted list.
This document discusses several Python programming tasks and provides explanations of bubble sort sorting algorithm. It includes:
1) Four programming tasks - writing a program to swap two numbers, search for an item in a list, find minimum and maximum of a list, and print a pattern using nested loops.
2) An explanation of bubble sort sorting algorithm with an example to sort an array from lowest to highest number using bubble sort in multiple passes.
3) Bubble sort algorithm and code in C language to sort an array using bubble sort.
4) Notes on multi-line comments and references for more information.
Merge sort analysis and its real time applicationsyazad dumasia
The document provides an analysis of the merge sort algorithm and its applications in real-time. It begins with introducing sorting and different sorting techniques. Then it describes the merge sort algorithm, explaining the divide, conquer, and combine steps. It analyzes the time complexity of merge sort, showing that it has O(n log n) runtime. Finally, it discusses some real-time applications of merge sort, such as recommending similar products to users on e-commerce websites based on purchase history.
The document provides instructions for 5 programming problems as part of a weekly challenge. Problem 1 involves writing a program to convert numbers to words. Problem 2 asks to merge two sorted text files. Problem 3 is to modify an encryption program to encrypt user input while preserving non-letter characters. Problem 4 requires generating banner text from a font file and user input. Problem 5 checks for duplicates in a Sudoku puzzle given as user input.
This document provides an overview of algorithms and asymptotic notation. It discusses that asymptotic notation allows algorithms to be compared based on how their running time grows relative to the input size. The key points covered include:
- Asymptotic notation describes the asymptotic behavior of a function, such as how fast algorithm running time grows relative to the input.
- Big O notation describes the worst case upper bound. If f(n) is O(g(n)), f(n) grows no faster than g(n).
- Common time complexities include O(1), O(log n), O(n), O(n log n), O(n^2).
- The dominating factor determines
This document provides instructions and sample code for Exercise 4 of Learn Python the Hard Way. The exercise introduces variables and variable names in Python.
The sample code defines several variables like cars, drivers, passengers, and uses them to calculate things like empty cars, carpool capacity, and average passengers per car. It prints the results.
The document provides extra credit tasks like adding comments to explain each line, reading the file backwards, finding typing errors, and researching floating point numbers. It also shows an example error when a variable is undefined.
A for loop is probably the most common type of loop in Python. A for loop will select items from any iterable. In Python an iterable is any container (list, tuple, set, dictionary), as well as many other important objects such as generator function, generator expressions, the results of builtin functions such as filter, map, range and many other items.
Spotle AI-thon - AI For Good Business Plan Showcase - Team IIM Indore - AI Ro...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore working on addressing mental health challenges and social problems with AI. Top 4 teams from IIM Lucknow, Cummins College, IIM Indore, made it to the final round in the business plan track. This is a showcase Top plan presentation from Team Champions Of IPBA - IIM Lucknow on building AI robots for COVID19 testing.
Spotle AI-thon - AI For Good Business Plan Showcase - Cummins CollegeSpotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore working on addressing mental health challenges and social problems with AI. Top 4 teams from IIM Lucknow, Cummins College, IIM Indore, made it to the final round in the business plan track. This is a showcase Top plan presentation from Team Cummins College, outlining an idea to address food wastage and security using AI.
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Team Elit...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore working on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Elite (IIM Indore, Aegis, Vardhaman College)
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India- Ankur chat...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Ankur chatterjee Gaurav Rai - DIAT
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Team La c...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team La Casa De Papel (Saurabh Agarwal and Dhruv Grover), IIT Roorkee
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Team Temp...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Temp, Chennai Mathematical Institute
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Team Zer...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Zeroth, NIT Jalandhar.
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Shivam Gi...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Shivam Kumar Giri - NIT Puducherry
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Cyber Pun...Spotle.ai
1) The document discusses analyzing emotions expressed on Twitter related to COVID-19 in India. It describes clustering tweets using unsupervised learning techniques like K-means, NMF, and LDA to group tweets by emotion (fear, joy, sadness, anger).
2) Supervised learning with a bidirectional LSTM model was also used, finding similar results of four emotion clusters.
3) The analysis found more negative tweets relating to fear, sadness, and anger than positive tweets related to joy, indicating people are not feeling optimistic about the COVID-19 situation.
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Tech Owls...Spotle.ai
Spotle AI-thon - The AI Global Challenge had 7000+ participants from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore, Charotar University, DIAT made it to the final round. This is a showcase Top 10 presentation from Team Tech Owls (Deepti Boddeda,
Soundarya Devi, Vruddhi Satra)
Spotle AI-thon Top 10 Showcase - Analysing Mental Health Of India - Team Jar...Spotle.ai
Spotle AI-thon - The AI Global Challenge 7000 participants had teams from best campuses in India, Singapore worked on addressing the mental health challenge with AI. Top 10 teams from IIT Roorkee, CMI, NIT, IIM Indore made it to the final round. This is a showcase Top 10 presentation from
Team Jarvis (AVIRUP CHAKRABORTY
Chennai Mathematical Institute
ROHAN GUPTA
Indian Institute of Technology Roorkee)
We are all consumers of financial services more or less. We have bank accounts, possibly life insurance, some of us have credit cards, some of us have fixed deposits, some of us may be doing share trading and investment, some of us are borrowers of loans. These are all financial services. Financial Technology or FinTech is a way of delivering or improving the delivery of financial services using technology and innovation.
The use of smartphones and internet to improve the services in banking, investing, lending and borrowing etc are examples of technologies aiming to make financial services more accessible to the people. The use of Artificial intelligence, Machine learning, Blockchain, Cryptocurrency etc are redefining the way we are used to receiving financial services. FinTech is an emerging industry. Startups, established financial institutions as well as technology companies are disrupting this space to replace or enhance the usage of currently existing financial services.
In this video we will restrict ourselves to the usage of AI in FinTech.
We will learn about different areas where FinTech is already serving a great deal.
We will learn about the areas where we look forward to seeing more disruptions and innovations to make financial services more secure and accessible to the general public.
Semi-supervised machine learning is a technique that uses both labeled and unlabeled data for training. It begins by taking a large unlabeled dataset and labeling a small portion of it. Then it uses unsupervised learning to cluster the unlabeled data and supervised learning on the labeled data to classify the remaining unlabeled data. Some applications of semi-supervised learning include internet content classification, where it is not feasible to label all webpages, and audio/video analysis, where the volume of files makes complete labeling impossible.
How do AI games work? How does Deep Blue, the supercomputer, beat chess champion Gary Kasparov. The answer is reinforcement learning.
This presentation develops your basic understanding of Reinforcement Learning, a key sub-discipline of Artificial Intelligence, with a number of examples from AI gaming and other exciting fields.
This slideshow will explain the present and future usage of AI in FinTech. The author has identified more than 10 areas where AI is positioned to play a very important role in FinTech.
Alan Turing first proposed the concept of artificial intelligence in 1950 and suggested computers could be taught to solve problems like humans. Early AI research was limited by computers' inability to store commands and programs. In the 1950s, the Logic Theorist program demonstrated rudimentary problem-solving skills. Advances in computing power and the introduction of machine learning algorithms and expert systems expanded AI research from the 1950s-1980s. Deep learning techniques in the 1980s and availability of neural networks in the 2000s enabled computers to learn from experience and tackle complex tasks in areas like language processing and computer vision.
The document discusses cybersecurity threats from AI and outlines several types of threats:
1) Corrupt learning and data sabotaging, where incorrect or altered training data causes AI systems to produce unreliable results.
2) Deepfaking identities and businesses, using AI to generate fake audio or video to impersonate individuals or executives.
3) Compromised secrets, through advanced AI cracking analytics leaked via employee data sharing.
4) Advanced malware powered by AI that could severely disrupt digital life through cyber attacks.
Robotic Process Automation With Blue PrismSpotle.ai
Robotic Process Automation is one of the future skills designated by Nasscom. Take a deep dive into the niche field and understand the nuances of RPA with Blue Prism, a clear market leader in the segment.
Streamlining End-to-End Testing Automation with Azure DevOps Build & Release Pipelines
Automating end-to-end (e2e) test for Android and iOS native apps, and web apps, within Azure build and release pipelines, poses several challenges. This session dives into the key challenges and the repeatable solutions implemented across multiple teams at a leading Indian telecom disruptor, renowned for its affordable 4G/5G services, digital platforms, and broadband connectivity.
Challenge #1. Ensuring Test Environment Consistency: Establishing a standardized test execution environment across hundreds of Azure DevOps agents is crucial for achieving dependable testing results. This uniformity must seamlessly span from Build pipelines to various stages of the Release pipeline.
Challenge #2. Coordinated Test Execution Across Environments: Executing distinct subsets of tests using the same automation framework across diverse environments, such as the build pipeline and specific stages of the Release Pipeline, demands flexible and cohesive approaches.
Challenge #3. Testing on Linux-based Azure DevOps Agents: Conducting tests, particularly for web and native apps, on Azure DevOps Linux agents lacking browser or device connectivity presents specific challenges in attaining thorough testing coverage.
This session delves into how these challenges were addressed through:
1. Automate the setup of essential dependencies to ensure a consistent testing environment.
2. Create standardized templates for executing API tests, API workflow tests, and end-to-end tests in the Build pipeline, streamlining the testing process.
3. Implement task groups in Release pipeline stages to facilitate the execution of tests, ensuring consistency and efficiency across deployment phases.
4. Deploy browsers within Docker containers for web application testing, enhancing portability and scalability of testing environments.
5. Leverage diverse device farms dedicated to Android, iOS, and browser testing to cover a wide range of platforms and devices.
6. Integrate AI technology, such as Applitools Visual AI and Ultrafast Grid, to automate test execution and validation, improving accuracy and efficiency.
7. Utilize AI/ML-powered central test automation reporting server through platforms like reportportal.io, providing consolidated and real-time insights into test performance and issues.
These solutions not only facilitate comprehensive testing across platforms but also promote the principles of shift-left testing, enabling early feedback, implementing quality gates, and ensuring repeatability. By adopting these techniques, teams can effectively automate and execute tests, accelerating software delivery while upholding high-quality standards across Android, iOS, and web applications.
Flutter vs. React Native: A Detailed Comparison for App Development in 2024dhavalvaghelanectarb
Choosing the right framework for your cross-platform mobile app can be a tough decision. Both Flutter and React Native offer compelling features and have earned their place in the development world. Here is a detailed comparison to help you weigh their strengths and weaknesses. Here are the pros and cons of developing mobile apps in React Native vs Flutter.
Enhanced Screen Flows UI/UX using SLDS with Tom KittPeter Caitens
Join us for an engaging session led by Flow Champion, Tom Kitt. This session will dive into a technique of enhancing the user interfaces and user experiences within Screen Flows using the Salesforce Lightning Design System (SLDS). This technique uses Native functionality, with No Apex Code, No Custom Components and No Managed Packages required.
Software Test Automation - A Comprehensive Guide on Automated Testing.pdfkalichargn70th171
Moving to a more digitally focused era, the importance of software is rapidly increasing. Software tools are crucial for upgrading life standards, enhancing business prospects, and making a smart world. The smooth and fail-proof functioning of the software is very critical, as a large number of people are dependent on them.
🏎️Tech Transformation: DevOps Insights from the Experts 👩💻campbellclarkson
Connect with fellow Trailblazers, learn from industry experts Glenda Thomson (Salesforce, Principal Technical Architect) and Will Dinn (Judo Bank, Salesforce Development Lead), and discover how to harness DevOps tools with Salesforce.
Penify - Let AI do the Documentation, you write the Code.KrishnaveniMohan1
Penify automates the software documentation process for Git repositories. Every time a code modification is merged into "main", Penify uses a Large Language Model to generate documentation for the updated code. This automation covers multiple documentation layers, including InCode Documentation, API Documentation, Architectural Documentation, and PR documentation, each designed to improve different aspects of the development process. By taking over the entire documentation process, Penify tackles the common problem of documentation becoming outdated as the code evolves.
https://www.penify.dev/
Transforming Product Development using OnePlan To Boost Efficiency and Innova...OnePlan Solutions
Ready to overcome challenges and drive innovation in your organization? Join us in our upcoming webinar where we discuss how to combat resource limitations, scope creep, and the difficulties of aligning your projects with strategic goals. Discover how OnePlan can revolutionize your product development processes, helping your team to innovate faster, manage resources more effectively, and deliver exceptional results.
A Comprehensive Guide on Implementing Real-World Mobile Testing Strategies fo...kalichargn70th171
In today's fiercely competitive mobile app market, the role of the QA team is pivotal for continuous improvement and sustained success. Effective testing strategies are essential to navigate the challenges confidently and precisely. Ensuring the perfection of mobile apps before they reach end-users requires thoughtful decisions in the testing plan.
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
Stork Product Overview: An AI-Powered Autonomous Delivery FleetVince Scalabrino
Imagine a world where instead of blue and brown trucks dropping parcels on our porches, a buzzing drove of drones delivered our goods. Now imagine those drones are controlled by 3 purpose-built AI designed to ensure all packages were delivered as quickly and as economically as possible That's what Stork is all about.
Superpower Your Apache Kafka Applications Development with Complementary Open...Paul Brebner
Kafka Summit talk (Bangalore, India, May 2, 2024, https://events.bizzabo.com/573863/agenda/session/1300469 )
Many Apache Kafka use cases take advantage of Kafka’s ability to integrate multiple heterogeneous systems for stream processing and real-time machine learning scenarios. But Kafka also exists in a rich ecosystem of related but complementary stream processing technologies and tools, particularly from the open-source community. In this talk, we’ll take you on a tour of a selection of complementary tools that can make Kafka even more powerful. We’ll focus on tools for stream processing and querying, streaming machine learning, stream visibility and observation, stream meta-data, stream visualisation, stream development including testing and the use of Generative AI and LLMs, and stream performance and scalability. By the end you will have a good idea of the types of Kafka “superhero” tools that exist, which are my favourites (and what superpowers they have), and how they combine to save your Kafka applications development universe from swamploads of data stagnation monsters!
Baha Majid WCA4Z IBM Z Customer Council Boston June 2024.pdfBaha Majid
IBM watsonx Code Assistant for Z, our latest Generative AI-assisted mainframe application modernization solution. Mainframe (IBM Z) application modernization is a topic that every mainframe client is addressing to various degrees today, driven largely from digital transformation. With generative AI comes the opportunity to reimagine the mainframe application modernization experience. Infusing generative AI will enable speed and trust, help de-risk, and lower total costs associated with heavy-lifting application modernization initiatives. This document provides an overview of the IBM watsonx Code Assistant for Z which uses the power of generative AI to make it easier for developers to selectively modernize COBOL business services while maintaining mainframe qualities of service.
A neural network is a machine learning program, or model, that makes decisions in a manner similar to the human brain, by using processes that mimic the way biological neurons work together to identify phenomena, weigh options and arrive at conclusions.
14 th Edition of International conference on computer visionShulagnaSarkar2
About the event
14th Edition of International conference on computer vision
Computer conferences organized by ScienceFather group. ScienceFather takes the privilege to invite speakers participants students delegates and exhibitors from across the globe to its International Conference on computer conferences to be held in the Various Beautiful cites of the world. computer conferences are a discussion of common Inventions-related issues and additionally trade information share proof thoughts and insight into advanced developments in the science inventions service system. New technology may create many materials and devices with a vast range of applications such as in Science medicine electronics biomaterials energy production and consumer products.
Nomination are Open!! Don't Miss it
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For Enquiry: Computer@scifat.com
Mobile App Development Company In Noida | Drona InfotechDrona Infotech
React.js, a JavaScript library developed by Facebook, has gained immense popularity for building user interfaces, especially for single-page applications. Over the years, React has evolved and expanded its capabilities, becoming a preferred choice for mobile app development. This article will explore why React.js is an excellent choice for the Best Mobile App development company in Noida.
Visit Us For Information: https://www.linkedin.com/pulse/what-makes-reactjs-stand-out-mobile-app-development-rajesh-rai-pihvf/
Unlock the Secrets to Effortless Video Creation with Invideo: Your Ultimate G...The Third Creative Media
"Navigating Invideo: A Comprehensive Guide" is an essential resource for anyone looking to master Invideo, an AI-powered video creation tool. This guide provides step-by-step instructions, helpful tips, and comparisons with other AI video creators. Whether you're a beginner or an experienced video editor, you'll find valuable insights to enhance your video projects and bring your creative ideas to life.
2. Spotle.ai Study Material
Spotle.ai/Learn
Gift him a shampoo
pack with instructions:
- Lather
- Rinse
- Repeat
How Do You Keep A Software Engineer
Shampooing Forever?
Img Src: Wikipedia
2
5. Spotle.ai Study Material
Spotle.ai/Learn
What Is An Algorithm?
Img Src: pandora
5
The sequence of
steps to solve a
specific problem or
attain a specific
goal is called an
algorithm.
6. Spotle.ai Study Material
Spotle.ai/Learn
Machines need clear
instructions to solve a
problem optimally. You need
to feed your machine the
right algorithm to get the
correct result within
minimum resources (time,
CPU units, memory).
Why Are Algorithms Important?
Img Src: pchelp
6
8. Spotle.ai Study Material
Spotle.ai/Learn
As a software engineer, your
key responsibility will be to
understand business
problems and find the best
path to solve them. In short
this is what your daily task
will look like:
PROBLEM -----------> SOLUTION
Algorithm
What Software Engineers Do
8
11. Spotle.ai Study Material
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Time complexity of
an algorithm denotes the amount
of time taken by an algorithm to
run, expressed as a function of
the length of the input. Time
complexity actually measures the
number of steps the algorithm
executes rather than the actual
time taken which depends also on
factors like processor speed,
network load etc.
Time Complexity
11
13. Spotle.ai Study Material
Spotle.ai/Learn
The Big O
13
My
algorithms
are O(1).
Big O describes the
performance or complexity of
an algorithm. It denotes the
worst-case scenario, and can
be used to describe the
execution time required or the
space used (e.g. in memory or
on disk) by an algorithm. The
Big O denotes the order of the
algorithm as a function of its
input size: eg O(1), O(n),
O(logn).
14. Spotle.ai Study Material
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O(1) algorithms always take a
constant amount of time
irrespective of the size of the
input.
For example, the algorithm to
check if the first number in a
list is 5 will always take
constant time irrespective of
the size of the list.
O(1) Algorithms
14
1
3
4
5
8
3
2
22
223
O(1) O(1)
Is this
5?
Is this
5?
15. Spotle.ai Study Material
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Say you have a jumbled up list of 8
numbers. You have to find if number 5
is in the list. What will you do?
Pick each number
If number = 5, terminate.
If number is not equal to 5, keep
number aside. Search the rest of the
numbers. In the worst case you have
to do this n = 8 times. The algorithm
has complexity = O(n).
O(n) Algorithms
15
3
100
15
2
1
8
20
5
Is this 5?
Is this 5?
Is this 5?
Is this 5?
Is this 5?
Is this 5?
Is this 5?
Is this 5?
16. Spotle.ai Study Material
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Say the list in the last
example is now sorted. How
much time will you take to
find 5 in the list?
O(logn) Algorithms
16
1
2
5
7
15
20
22
100
17. Spotle.ai Study Material
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Pass 1: Divide the list into 2.
Compare 5 to the largest
(bottom-most number) in
upper partition. Since 5 < 7,
5 will be in the upper
partition. Discard lower
partition.
O(logn) Algorithms
17
1
2
5
7
15
20
22
100
18. Spotle.ai Study Material
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Pass 2: Divide the list from
pass1 into 2 halves.
Compare 5 with the largest
number in the upper
partition. Since 5 > 2, 5 must
be in the lower partition.
Discard the upper partition
O(logn) Algorithms
18
1
2
5
7
19. Spotle.ai Study Material
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Pass 3: Divide the list from
pass 2 into 2 halves.
Compare 5 with the largest
number (the lower-most
number) in the upper
partition. You have found
your 5!
O(logn) Algorithms
19
5
7
20. Spotle.ai Study Material
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In this algorithm you are
halving the input set till you
have partitions of 1. How
many times do you have to
repeat the operation of
halving the set?
Let k be the number of times
the set is halved
Then n/2k = 1 or k = log2n
O(logn) Algorithms
20
5
7
1
2
5
7
15
20
22
100
1
2
5
7
21. Spotle.ai Study Material
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The Problem: Find the smallest
number in the list
The easiest solution is to take
each number in the list and
compare to all other numbers
in the list to see if it is the
smallest number. For a list of
size 3, in the worst case this
will be done 3 x 3 times or 9
times. The algorithm has
complexity O(n2)
O(n2) Algorithms
21
20
7
5
20
7
5
O(n2) Comparisons
22. Spotle.ai Study Material
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Is there a more efficient way of
finding the smallest number in a
list?
Lets designate the first number in
the list, in this case 20 as the
smallest number. Compare each
number in the list with the
smallest number. If the number is
smaller than the smallest number,
set smallest number = number.
You are repeating this n=3 times.
The complexity is O(n).
Reducing Algorithmic Complexity
22
20
5
7
20
20
5
7
5
20
5
7
5
1st pass.
20 is taken
to be smallest
2nd pass.
5 is taken
to be smallest;
because 5 is
smaller than 20.
3rd pass.
5 stands as the
smallest;
because 5 is
smaller than 7.
23. Spotle.ai Study Material
Spotle.ai/Learn
While designing
complex systems, large
systems are broken
down into modules.
This process is called
modularization.
Modular Approach To System Design
Img Src: Wikipedia
23
Design Engine
Design Body
Design Storage