RNN Architecture and Sentiment Classification

Overview

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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 ManyToMany, ManyToOne, and OneToMany 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 PyTorch, sentiment classification applications, and hands-on exercises. By the end, you'll develop and apply various RNN models for tasks like sentiment analysis and language modeling, understand fixed-length and infinite memory models, utilize PyTorch for building and optimizing RNN models, and perform advanced tasks like gradient descent and backpropagation through time. Designed for data scientists, machine learning engineers, and AI enthusiasts with basic programming and neural network knowledge, the course combines theory with hands-on application via video tutorials and real-world examples.

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.