RNN Architecture and Sentiment Classification

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Overview

Artificial Intelligence is revolutionizing data analysis. This course delves into Recurrent Neural Networks (RNNs), starting with basic memory models and advancing to deep RNN structures. You'll explore RNN models like Many To Many, Many To One, and One To Many through practical exercises, culminating in sentiment classification for sophisticated text analysis and prediction. You will gain a solid grasp of RNN architectures and implement sentiment classification models. Key features include detailed RNN architecture, practical implementation using Py Torch, sentiment classification...

Syllabus

RNN Architecture

In this module, we will explore the fundamental structures of Recurrent Neural Network (RNN) architectures. You'll learn about fixed length memory models, infinite memory architectures, and various model configurations such as Many-to-Many, Many-to-One, and One-to-Many. Through exercises and practical activities, you'll gain a deep understanding of these architectures and their applications.

Gradient Descent in RNN

In this module, we will delve into the gradient descent algorithm as it applies to Recurrent Neural Networks. You'll learn the fundamental equations, understand the role of gradients, and apply the chain rule. Practical exercises and examples will illustrate backpropagation through time, ensuring a comprehensive grasp of these essential techniques.

RNN Implementation

In this module, we will focus on the practical implementation of RNNs. You'll learn about automatic differentiation in PyTorch, and apply RNNs to language modeling and next word prediction tasks. Through step-by-step coding exercises, you'll develop hands-on skills in building and training RNN models for language-related applications.

Sentiment Classification Using RNN

In this module, we will tackle sentiment classification using Recurrent Neural Networks. You'll learn how to implement vocabulary and vectorizers, set up RNN models, and train them for sentiment analysis. Practical exercises will guide you through each step, ensuring you can effectively classify text data based on sentiment.