Artificial Intelligence is transforming industries by enabling machines to learn from data and make intelligent decisions. This course offers an in-depth exploration of Recurrent Neural Networks (RNN) and Deep Neural Networks (DNN), two pivotal AI technologies.
Introduction to RNN and DNN
Introduction to RNN and DNN
This course is part of Deep Learning: Recurrent Neural Networks with Python Specialization
Instructor: Packt - Course Instructors
Included with 
Gain insight into a topic and learn the fundamentals.
Beginner 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
Utilize PyTorch to build and optimize AI models.
Examine the effectiveness of gradient descent and hyperparameter tuning in model optimization.
Develop and apply RNN models for complex tasks such as speech recognition and machine translation.
Skills you'll gain
Details to know
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Recently updated!
September 2024
Assessments
1 assignment
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 3 modules in this course
In this module, we will introduce you to the course instructor, providing insights into their background and expertise. Additionally, we will outline the primary focus and objectives of the course, setting the stage for your learning journey in AI sciences.
What's included
2 videos2 readings
In this module, we will delve into the diverse applications of Recurrent Neural Networks (RNNs). You will learn to recognize human activities in videos, generate image captions, perform machine translation, and implement speech recognition. We will also explore using RNNs for stock price predictions and determine appropriate scenarios for modeling RNNs.
What's included
7 videos
In this module, we will explore the fundamentals of Deep Neural Networks (DNNs) and their implementation using PyTorch. You will learn about the architecture and representational power of DNNs, understand the importance of activation functions, and get hands-on experience with perceptrons. We will also cover gradient descent techniques, loss functions, and optimization strategies for building and refining DNN models.
What's included
45 videos1 reading1 assignment
Instructor
Offered by
Recommended if you're interested in Machine Learning
Johns Hopkins University
DeepLearning.AI
DeepLearning.AI
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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.