# Hyperparameter Tuning {% embed url="" %} Hyperparameter Tuning - Infrastructure and Tooling {% endembed %} ## Summary * Deep learning models are literally full of hyper-parameters. Finding the best configuration for these variables in a high-dimensional space is not trivial. * Searching for hyper-parameters is an iterative process constrained by computing power, money, and time. Therefore, it would be really useful to have software that helps you search over hyper-parameter settings. * **Hyperopt** is a Python library for serial and parallel optimization over awkward search spaces, which may include real-valued, discrete, and conditional dimensions. * **SigOpt** is an optimization-as-a-service API that allows users to seamlessly tune the configuration parameters in AI and ML models. * [**Ray Tune**](https://docs.ray.io/en/latest/tune.html) is a Python library for hyperparameter tuning at any scale, integrating seamlessly with optimization libraries such as **Hyperopt** and **SigOpt**. * **Weights & Biases** has a nice feature called “Hyperparameter Sweeps” — a way to efficiently select the right model for a given dataset using the tool. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://fall2019.fullstackdeeplearning.com/course-content/infrastructure-and-tooling/hyperparameter-tuning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.