This advanced course on Recurrent Neural Networks (RNNs) addresses key challenges like the vanishing gradient problem and provides solutions such as Gated Recurrent Units (GRUs) and Long Short Term Memory (LSTM) networks.
Advanced RNN Concepts and Projects
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Advanced RNN Concepts and Projects
This course is part of Deep Learning: Recurrent Neural Networks with Python Specialization
Taught in English
Instructor: Packt
Included with 
Course
Gain insight into a topic and learn the fundamentals
Advanced level
Recommended experience
6 hours (approximately)
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.
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Recently updated!
September 2024
Assessments
3 assignments
Course
Gain insight into a topic and learn the fundamentals
Advanced level
Recommended experience
6 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
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.
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- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
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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
Recommended if you're interested in Machine Learning
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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.