15 Ways to Optimize Neural Network Training (With Implementation)

I published an infographic recently about 15 techniques to optimize model training.

Many of you showed interest in a structured guide with implementations.

Our latest article discusses this in detail: 15 Ways to Optimize Neural Network Training (With Implementation).

Each technique is backed by code examples to help you implement these optimizations in your projects.

Why care?

No ML tech company considers model training as a core skill in MLE roles.

Instead, what they care about is your understanding of the science behind the model and whether you can apply the right techniques to get the most efficient results.

Think about it.

There’s a reason why the “ML engineer” job role exists, but you will never find, say, an “ML model developer.”

This is obvious since the real challenge often lies not just in designing a model but in efficiently engineering its training process, which requires deep expertise like:

• How to identify bottlenecks in existing model training procedures?

• Which specific techniques could help?

• What are the trade-offs of using those techniques?

• Are there any hardware limitations for those techniques?

If you genuinely wish to add value as a machine learning engineer and help your employer save operational costs in real-world projects, don’t overlook this skill.

If you need help with developing this skill, this will be useful: 15 Ways to Optimize Neural Network Training (With Implementation).

Have a good day!

Avi