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⇱ Optimize PyTorch: Build and Accelerate Layers | Coursera

Optimize PyTorch: Build and Accelerate Layers

Optimize PyTorch: Build and Accelerate Layers

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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!

March 2026

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English

Build your subject-matter expertise

This course is part of the Deep Learning Engineering 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

There is 1 module in this course

Learn to build custom neural-network layers and accelerate model training with performance-driven PyTorch techniques. This hands-on, engineer-focused course teaches you how to design differentiable modules, diagnose bottlenecks, and apply optimizations like mixed precision and gradient accumulation to significantly boost training throughput.

Learn to build custom neural-network layers and accelerate model training with performance-driven PyTorch techniques. This hands-on, engineer-focused course teaches you how to design differentiable modules, diagnose bottlenecks, and apply optimizations like mixed precision and gradient accumulation to significantly boost training throughput.

What's included

6 videos2 readings5 assignments

6 videos•Total 31 minutes
  • Why Custom Layers Matter in PyTorch•5 minutes
  • Tensor Operations & Autograd: How PyTorch Tracks Your Computations•4 minutes
  • Coding a Squeeze-and-Excite Layer in Pytorch•3 minutes
  • Profiling Your Training Loop with PyTorch •7 minutes
  • Accelerating Training with FP16 and Gradient Accumulation•9 minutes
  • Congratulations and Continuous Learning Journey•3 minutes
2 readings•Total 18 minutes
  • How PyTorch Tracks Your Computations•10 minutes
  • Diagnosing GPU Bottlenecks: Improving PyTorch Training Throughput •8 minutes
5 assignments•Total 79 minutes
  • Graded Quiz: PyTorch Autograd, Custom Layers, and Training Performance•20 minutes
  • HOL: Design a Custom PyTorch Layer with Autograd in Mind•20 minutes
  • Practice Quiz: Core PyTorch & Deep Learning Concepts Check•7 minutes
  • HOL: Boost Training Throughput with Profiling, FP16, and Gradient Accumulation•25 minutes
  • Practice Quiz: Training Performance and Optimization Fundamentals •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.