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.
RNN Architecture and Sentiment Classification
This course is part of Deep Learning: Recurrent Neural Networks with Python Specialization
Recommended experience
What you'll learn
Identify different RNN architectures, including fixed-length and infinite memory models.
Examine the effectiveness of gradient descent and backpropagation through time in training RNN models.
Develop and apply RNN models for advanced tasks such as sentiment analysis and language modeling.
Skills you'll gain
Details to know
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September 2024
3 assignments
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There are 4 modules in this course
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.
What's included
30 videos2 readings
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.
What's included
13 videos1 assignment
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.
What's included
11 videos
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.
What's included
7 videos1 reading2 assignments
Recommended if you're interested in Data Analysis
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Frequently asked questions
Yes, you can preview the first video and view the syllabus before you enroll. You must purchase the course to access content not included in the preview.
If you decide to enroll in the course before the session start date, you will have access to all of the lecture videos and readings for the course. You’ll be able to submit assignments once the session starts.
Once you enroll and your session begins, you will have access to all videos and other resources, including reading items and the course discussion forum. You’ll be able to view and submit practice assessments, and complete required graded assignments to earn a grade and a Course Certificate.