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URL: https://www.coursera.org/learn/design-and-build-custom-neural-networks

⇱ Design and Build Custom Neural Networks | Coursera

Design and Build Custom Neural Networks

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Design and Build Custom Neural Networks

This course is part of multiple programs.

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

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

2 hours to complete
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

Add to your LinkedIn profile

Recently updated!

February 2026

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English

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There is 1 module in this course

This course teaches you how to evaluate and design custom neural network architectures for real machine-learning tasks. You start by learning how to compare common model families—such as CNNs, RNNs, and Transformers—and match them to task needs, data patterns, and compute limits. You then learn how to construct custom architectures using layers, activations, and regularization techniques that improve generalization and training stability. Through videos, readings, hands-on practice, and guided coach support, you build models in PyTorch and test how design choices affect performance. By the end of the course, you can confidently select topologies, justify architectural decisions, and design models ready for real-world deployment.

This course teaches you how to evaluate and design custom neural network architectures for real machine-learning tasks. You start by learning how to compare common model families—such as CNNs, RNNs, and Transformers—and match them to task needs, data patterns, and compute limits. You then learn how to construct custom architectures using layers, activations, and regularization techniques that improve generalization and training stability. Through videos, readings, hands-on practice, and guided coach support, you build models in PyTorch and test how design choices affect performance. By the end of the course, you can confidently select topologies, justify architectural decisions, and design models ready for real-world deployment.

What's included

7 videos2 readings5 assignments

7 videos•Total 20 minutes
  • Welcome and Why Architecture Choices Matter•2 minutes
  • Comparing Neural Network Topologies•3 minutes
  • How to Evaluate Architecture Fit in Practice•3 minutes
  • Why Build Custom Architectures•2 minutes
  • Layers, Activations, and Regularization•2 minutes
  • Screencast: Constructing a Custom Model in PyTorch•5 minutes
  • Congratulations and Continuous Learning Journey•2 minutes
2 readings•Total 20 minutes
  • Understanding Task, Data, and Compute Constraints•10 minutes
  • Designing a Custom Network Step by Step•10 minutes
5 assignments•Total 64 minutes
  • Graded Assessment: Custom Neural Network Architechture Evaluation•20 minutes
  • HOL: Choose the Best Architecture Under Real Constraints•15 minutes
  • Practice Quiz: Architecture Selection Mini-Review•7 minutes
  • Hands-on Activity: Build Your Own Network Architecture•15 minutes
  • Practice Quiz: Improve a Baseline Model With Regularization•7 minutes

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.