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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 …
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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 …
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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 …
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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 …
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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 …
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