Packt

Introduction to RNN and DNN

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
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.

Details to know

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Recently updated!

September 2024

Assessments

1 assignment

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.
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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 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

Packt
Packt
170 Courses2,469 learners

Offered by

Packt

Recommended if you're interested in Machine Learning

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