Introduction to Recurrent Neural Networks

Introduction to Recurrent Neural Networks

RNNs are designed to recognize patterns in sequences of data, such as time series or text. They achieve this by maintaining a hidden state that is updated at each time step based on the current input and the previous hidden state. This allows RNNs to capture temporal dependencies in the data.The basic structure of an RNN consists of:...

Building an RNN from Scratch in Pytorch

Setting Up the Environment...

Example 1: Predicting Sequential Data: An RNN Approach Using PyTorch

To use an RNN to predict the next value in a series of numbers, we will build a basic synthetic dataset. This will assist us in comprehending the fundamentals of RNN operation and PyTorch implementation. Step-by-Step Implementation:...

Example 2: Sentiment Analysis with RNN: Classifying Movie Reviews Using PyTorch

In this example, we will use a public dataset to perform sentiment analysis on movie reviews. The goal is to classify each review as positive or negative using an RNN. Step-by-Step Implementation:...

Conclusion

In this article, we have implemented a simple RNN from scratch using PyTorch. We covered the basics of RNNs, built an RNN class, trained it on a sine wave prediction task, and evaluated its performance. This implementation serves as a foundation for more complex RNN architectures and tasks....