📚 Collection of various Data Structures and Algorithms, implemented in Kotlin for educational purposes.
📚 Algorithms, Data Structures, Design Patterns, Concurrency in Kotlin
Kotlin solutions of the LeetCode Top Interview Questions.
Penerapan Weighted PRNG dengan studi kasus Gacha pada game Genshin Impact
Collection of solutions for algorithm problems from Codility and other sources in Kotlin.
Solutions to problems found in Elements of programming interviews book
FFT is an efficient algorithm for computing the discrete Fourier transform (DFT) of a sequence, enabling the frequency analysis of signals in linear time. It plays a crucial role in various applications, including image processing, audio compression, and cryptography.
🎯 Soluções propostas para os problemas do HackerRank
Server-driven UI refers to a design pattern in which the user interface is primarily controlled and rendered by a server, with the client serving as a display and interaction layer. This approach allows for a separation of concerns between the presentation and business logic, and can simplify client-side development.
The Knuth-Morris-Pratt (KMP) algorithm is a linear time pattern matching algorithm that efficiently searches for occurrences of a pattern in a text. It pre-processes the pattern to determine a partial match table which is used to quickly skip over sections of the text that cannot match the pattern.
A collection of data structures and algorithms in various languages for practicing purposes
Approximation algorithms are algorithms that find approximate solutions to optimization problems, usually with a guarantee of the solution's quality relative to the optimal solution. They are used when exact solutions are too time-consuming to compute. Approximation algorithms trade-off optimality for efficiency and are commonly used in scheduling.
Apprendre Kotlin en résolvant les problèmes de advent of code 2018
Base algorithms implementations, base data structure implementations, Leetcode problems solutions
.A Generative Adversarial Network (GAN) is a deep learning architecture used to generate new data that resembles existing data. It consists of two neural networks, a generator and a discriminator, that are trained in competition with each other. The generator creates synthetic data, while the discriminator tries to distinguish between real.
Learning Kotlin lang while solving hackerrank challenges
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