Packt
RNN Architecture and Sentiment Classification
Packt

RNN Architecture and Sentiment Classification

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

7 hours to complete
3 weeks at 2 hours a week
Flexible schedule
Learn at your own pace

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.

Details to know

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Recently updated!

September 2024

Assessments

3 assignments

Taught in English

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This course is part of the Deep Learning: Recurrent Neural Networks with Python Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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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

Instructor

Packt - Course Instructors
Packt
375 Courses25,243 learners

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Packt

Recommended if you're interested in Data Analysis

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