Packt
Advanced RNN Concepts and Projects
Packt

Advanced RNN Concepts and Projects

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

Recommended experience

6 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.
Advanced level

Recommended experience

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

What you'll learn

  • Identify key components and functionalities of GRUs, LSTMs, and attention mechanisms.

  • Utilize TensorFlow to build, train, and optimize RNN models.

  • Develop and implement advanced RNN models to solve complex problems.

Details to know

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Recently updated!

September 2024

Assessments

3 assignments

Taught in English

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Build your subject-matter expertise

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.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate
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There are 5 modules in this course

In this module, we will address the vanishing gradient problem in Recurrent Neural Networks and explore various solutions. You'll learn about Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks, including their mathematical foundations. Additionally, we will cover bidirectional RNNs and the attention model, providing a comprehensive approach to improving RNN performance.

What's included

9 videos2 readings

In this module, we will introduce you to TensorFlow, a powerful framework for building and training deep learning models. You will learn how to implement TensorFlow in practical applications, focusing on a text classification example using RNNs. Additionally, we'll compare TensorFlow with other popular deep learning frameworks to highlight its strengths and unique features.

What's included

2 videos1 assignment

In this module, we will guide you through your first project: creating a book writer using RNNs. You will learn to map data, prepare the RNN architecture, and train the model using TensorFlow. By the end, you'll be able to generate coherent text and complete an activity to build a word-level text generator.

What's included

7 videos

In this module, we will tackle the stock price prediction project. You will learn to define the problem, create and prepare a dataset, and train an RNN model. Through practical exercises, you will gain experience in evaluating the model's performance and implementing an artificial neural network for stock prediction.

What's included

5 videos1 assignment

In this module, we will provide you with further reading and resources to expand your knowledge beyond the course. You'll have access to curated materials that will support your continued learning and mastery of Recurrent Neural Networks and their applications.

What's included

1 video1 reading1 assignment

Instructor

Packt - Course Instructors
Packt
372 Courses9,744 learners

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Packt

Recommended if you're interested in Machine Learning

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