https://blog.sksoumik.com/tags/algorithm/ Recent content in algorithm on Sadman Kabir Soumik Hugo -- gohugo.io en Copyright © 2022, Sadman Kabir Soumik Mon, 30 Jan 2023 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/from-rnn-to-transformers-without-math/ Mon, 30 Jan 2023 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/from-rnn-to-transformers-without-math/ Transformer-based models are a types of neural network architecture that uses self-attention mechanisms to process input data. They were introduced in the paper "Attention Is All You Need" by Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin in 2017, and have since become a popular choice for many natural language processing task. Prerequisite: Before going further, I assume that you have a basic understanding of how neural networks work. https://blog.sksoumik.com/artificial-intelligence/comparing-random-forest-decision-tree-gradient-boosting/ Sat, 27 Mar 2021 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/comparing-random-forest-decision-tree-gradient-boosting/ Decision Tree, Random Forest (RF), and Gradient Boosting (GB) are three popular algorithms used for supervised learning tasks such as classification and regression. In this blog, we will compare these three algorithms in terms of their features, performance, and usability. Decision Tree is a simple and intuitive algorithm that can be used for classification and regression tasks. A Decision Tree model is built by recursively partitioning the training data into smaller and smaller subsets based on the values of the input features. https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Sun, 25 Oct 2020 00:00:00 +0000 https://blog.sksoumik.com/artificial-intelligence/understanding-recurrent-neural-networks/ Recurrent Neural Network A recurrent neural network (RNN), is a type of neural network that can process sequential data, like text, audio, or time series data. Here's how it works: first, the RNN takes in some input data, which could be a word in a sentence, a sound wave from an audio recording, or a measurement from a sensor at a specific time. Then, the RNN processes this input and generates an output, which could be a predicted next word in a sentence, a generated audio waveform, or a predicted sensor measurement. https://blog.sksoumik.com/software-engineering/dynamic-programming-step-by-step-explanation/ Mon, 23 Dec 2019 00:00:00 +0000 https://blog.sksoumik.com/software-engineering/dynamic-programming-step-by-step-explanation/ Dynamic programming is a method for solving complex problems by breaking them down into smaller subproblems. It is a mathematical optimization technique that is mainly used for solving problems that exhibit the properties of overlapping subproblems and optimal substructure. The basic idea behind dynamic programming is to solve a complex problem by breaking it down into smaller subproblems, solving each of those subproblems just once, and storing their solutions. The solutions to the subproblems are then used to solve the original problem. https://blog.sksoumik.com/software-engineering/dependency-injection-real-world-examples-advantages/ Sat, 10 Feb 2018 00:00:00 +0000 https://blog.sksoumik.com/software-engineering/dependency-injection-real-world-examples-advantages/ Dependency injection is a design pattern that is widely used in software development to promote good software design and make code more flexible and easier to test. In this blog post, we will take a closer look at what dependency injection is, how it works, and its advantages. What is Dependency Injection? Dependency injection is a technique that allows an object to receive its dependencies (other objects it needs to function) from the outside, rather than creating them itself.