Packt
Advanced CNNs, Transfer Learning, and Recurrent Networks
Packt

Advanced CNNs, Transfer Learning, and Recurrent Networks

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

Recommended experience

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

Recommended experience

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

What you'll learn

  • Apply transfer learning techniques to enhance model performance.

  • Utilize RNNs and LSTMs for sequence prediction tasks.

  • Develop practical solutions for industry-specific problems.

  • Master the integration of advanced neural networks in real-world applications.

Details to know

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Recently updated!

September 2024

Assessments

4 assignments

Taught in English

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This course is part of the Deep Learning with Real-World Projects 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 8 modules in this course

In this module, we will delve into the basics of CNNs, examining the VGG16 architecture, and engage in a comprehensive case study spread across multiple practical sessions. These hands-on exercises will reinforce the theoretical concepts covered.

What's included

7 videos2 readings

In this module, we will explore various pre-trained models, their architectures, and the principles of transfer learning. Through a series of detailed sessions, we will apply these concepts in practical settings, culminating in case studies and analytical discussions.

What's included

16 videos

In this module, we will apply CNN techniques to real-world natural images, specifically focusing on flower images. Through an extensive case study spread over multiple sessions, we will learn to implement, evaluate, and refine models in a practical, industry-relevant context.

What's included

15 videos1 assignment

In this module, we will tackle the challenge of identifying medical abnormalities using CNNs. Focusing on X-Ray images, we will conduct a detailed case study over several sessions, learning to interpret medical data and develop effective diagnostic models.

What's included

7 videos

In this module, we will introduce Recurrent Neural Networks, covering their basic concepts, architecture, and types. We will delve into training methods and address common challenges like the vanishing gradient problem through a series of detailed sessions.

What's included

12 videos

In this module, we will focus on Long Short-Term Memory (LSTM) networks, covering their architecture and functionality. We will compare LSTM with other RNN variants like GRU and implement these networks in practical scenarios through a series of detailed sessions.

What's included

10 videos1 assignment

In this module, we will apply RNN techniques to develop a Part-Of-Speech tagger for natural language processing tasks. Through an extended case study spread across multiple sessions, we will develop, evaluate, and refine the performance of the Part-Of-Speech tagger.

What's included

9 videos

In this module, we will delve into the practical application of RNNs for text generation by exploring a comprehensive code generator case study divided into four parts. Each part builds on the previous one, enhancing our understanding and skills in using RNNs for generating coherent text.

What's included

4 videos1 reading2 assignments

Instructor

Packt
Packt
196 Courses2,786 learners

Offered by

Packt

Recommended if you're interested in Machine Learning

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