Stop Using Grid Search! The Complete Practical Tutorial on Keras Tuner
Last Updated on July 25, 2023 by Editorial Team
Author(s): Konstantinos Poulinakis
Originally published on Towards AI.
Contents:
This member-only story is on us. Upgrade to access all of Medium.
Keras Tuner practical tutorial for automatic hyperparameter tuning of deep neural networks. An autoML tutorial.
Photo by Veri Ivanova on UnsplashIntroLoad dataBasics of Keras-TunerPutting it all together (code explanation)Execute the hyperparameter searchExtract and train the best modelBonus: Some Tips & TricksFinal thoughts
In this article, you will not only learn how to use KerasTuner but also some tricks that are not included in other tutorials, such as tuning parameters in each layer separately or tuning the learning rate in conjunction with the optimizer, which is not straightforward due to some limitations,… Read the full blog for free on Medium.
Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor.
Published via Towards AI
Towards AI Academy
We Build Enterprise-Grade AI. We'll Teach You to Master It Too.
15 engineers. 100,000+ students. Towards AI Academy teaches what actually survives production.
Start free — no commitment:
→ 6-Day Agentic AI Engineering Email Guide — one practical lesson per day
→ Agents Architecture Cheatsheet — 3 years of architecture decisions in 6 pages
Our courses:
→ AI Engineering Certification — 90+ lessons from project selection to deployed product. The most comprehensive practical LLM course out there.
→ Agent Engineering Course — Hands on with production agent architectures, memory, routing, and eval frameworks — built from real enterprise engagements.
→ AI for Work — Understand, evaluate, and apply AI for complex work tasks.
Note: Article content contains the views of the contributing authors and not Towards AI.