Welcoem to posts!!
in the future - u will be able to do some more stuff here,,,!! like pat catgirl- i mean um yeah... for now u can only see others's posts :c
Hey everyone! ✨
Part 5 of our Time & Space Complexity series is live now - join us!
Ever wondered how algorithms handle varying operation costs? ⏳💡
In this video, we delve into:
- Explaining amortized analysis for algorithms with mixed operation speeds.
- Contrasting amortized analysis with average-case analysis.
- Practical applications in analyzing dynamic arrays and similar scenarios.
Explore how algorithms manage diverse operation costs effectively!
Watch now to uncover the insights of amortized analysis! 🚀✨
#AmortizedAnalysis #AlgorithmPerformance #DynamicArrays
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Hey everyone! ✨
Part 4 of our Time & Space Complexity series is live now - watch now!
Ever wondered how algorithms handle different scenarios? 📊💻
In this video, we explore algorithm complexity:
- Explaining "best case," "average case," and "worst case" scenarios in algorithm performance.
- Using Big O notation to analyze worst-case performance guarantees.
- Exploring recursive functions and their divide-and-conquer strategy.
- Solving problems related to recursive code and analyzing time complexity.
- Understanding space complexity and its measurement in algorithms.
- Considering auxiliary space and its impact on algorithm efficiency.
- Practical problem-solving to analyze both time and space complexity.
Ready to optimize your algorithms for peak performance? Dive into algorithm complexity now! 🚀✨
#AlgorithmComplexity #TimeComplexity #SpaceComplexity #RecursiveFunctions
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As the #10dayschallenge has come to an end.
A huge shoutout to @jitendersingh9473 , the undeniable champion of our Time & space complexity quiz challenge, who answered all the questions correctly! 🌟🥇
Congratulations on claiming the top spot! As a token of our appreciation, you'll receive a special Algocamp t-shirt! 🎉🎁
Please send us your complete mailing address, mobile number, and email to algocamproot@gmail.com, and we will dispatch your prize very soon. 📬📱💻
Huge thanks to everyone who participated! Your enthusiasm made this challenge a remarkable success. We're sorry to leave you guys, but we'll return with a new challenge soon.🌟✨
Also, we have recently launched our Advanced Frontend Development Elite 2.0 course(Use coupon FRONTEND)
Dont forget to check out the course here - courses.algocamp.io/learn/Advanced-Frontend-Dev-wi…
#Winner #TopScorer #Congratulations #CommunityLove #tshirtComingYourWay #frontend #elite2.0
#challenge #quiz
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Hey Everyone! ✨
Part 3 of our Time & Space Complexity series is live now - join us!
Ever wondered how fast a program can run? In this video, we delve into time complexity and asymptotic notations:
- Explaining Omega (Ω), Theta (Θ), and Big O (O) notations.
- Understanding best-case, average-case, and worst-case scenarios in algorithm performance.
- Focusing on Big O notation for worst-case guarantees.
- Learning to calculate tight upper bounds using Big O.
- Solving problems to identify and express worst-case scenarios with Big O.
By the end, you'll grasp how algorithms scale with data size and confidently analyze their performance characteristics.
Watch now to master worst-case analysis in algorithms! 🚀✨
#TimeComplexity #AsymptoticNotations #BigO #WorstCaseAnalysis
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Hey Everyone! ✨
Part 3 of our Time & Space Complexity series is live now - join us!
Ever wondered how fast a program can run? In this video, we delve into time complexity and asymptotic notations:
- Explaining Omega (Ω), Theta (Θ), and Big O (O) notations.
- Understanding best-case, average-case, and worst-case scenarios in algorithm performance.
- Focusing on Big O notation for worst-case guarantees.
- Learning to calculate tight upper bounds using Big O.
- Solving problems to identify and express worst-case scenarios with Big O.
By the end, you'll grasp how algorithms scale with data size and confidently analyze their performance characteristics.
Watch now to master worst-case analysis in algorithms! 🚀✨
#TimeComplexity #AsymptoticNotations #BigO #WorstCaseAnalysis
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Day 10 of #10daychallenge
Scenario 10: Basic Understanding of Amortized Analysis
Situation: You're learning about amortized analysis to better understand average performance over time.
Objective: Understand the concept of amortized time complexity.
Question: What does amortized time complexity measure?
A. The worst-case time of a single operation
B. The average time per operation over a sequence of operations
C. The best-case time of a single operation
D. The total time of all operations divided by the number of operations
Comment down your answer to this question
Also, we have recently launched our Advanced Frontend Development Elite 2.0 course(Use coupon FRONTEND)
Check out the course here - courses.algocamp.io/learn/Advanced-Frontend-Dev-wi…
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Day 9 of #10daychallenge
Scenario 9: Space Complexity Basics
Situation: You're studying space complexity to understand its impact on algorithm efficiency.
Objective: Understand what space complexity measures.
Question: What does space complexity measure in an algorithm?
A. The time it takes to execute
B. The amount of memory it uses
C. The number of steps in the algorithm
D. The size of the input data
Comment down your answer to this question
Also, we have recently launched our Advanced Frontend Development Elite 2.0 course(Use coupon FRONTEND)
Check out the course here - courses.algocamp.io/learn/Advanced-Frontend-Dev-wi…
8 - 10
Day 8 of #10daychallenge
Scenario 8: Basic Asymptotic Notation
Situation: You're learning about different types of asymptotic notations for the first time.
Objective: Recognize the different types of asymptotic notations.
Question: Which notation describes the upper bound of an algorithm's running time?
A. Big O Notation
B. Omega Notation
C. Theta Notation
D. Delta Notation
Comment down your answer to this question
Also, we have recently launched our Advanced Frontend Development Elite 2.0 course(Use coupon FRONTEND)
Check out the course here - courses.algocamp.io/learn/Advanced-Frontend-Dev-wi…
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Day 7 of #10daychallenge
Scenario 7: Problem Solving with Algorithm Complexity
Situation: You're solving a problem to understand how algorithm complexity affects execution time.
Objective: Apply your knowledge to solve a practical problem.
Question: Given an algorithm with a time complexity of O(n^2), how does the execution time change if the input size doubles?
A. Remains the same
B. Doubles
C. Quadruples
D. Increases by a factor of n
Comment down your answer to this question
Also, we have recently launched our Advanced Frontend Development Elite 2.0 course(Use coupon FRONTEND)
Check out the course here - courses.algocamp.io/learn/Advanced-Frontend-Dev-wi…
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Hey Everyone! ✨
Part 2 of our Time & Space Complexity series is live now - watch now!
Ever noticed how adding more filters to search on Zomato slows down the process? It's all about how algorithms handle different amounts of data, known as input size.
This video explores algorithm efficiency:
- Visualizing algorithm performance with increasing data sizes using graphs.
- Understanding growth rates: how algorithms behave with large data sets.
- Using asymptotic analysis to compare algorithms based on their scalability.
- Learning to select the optimal algorithm for efficient program execution, regardless of data size.
By the end, you'll analyze algorithm complexity and confidently choose the best algorithms for your applications.
Watch now to master algorithm selection and optimize your programs! 🚀✨
#AlgorithmSelection #InputSize #AsymptoticAnalysis #Optimization
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