# Week 6 - Hyperparameter Optimization ### This week you will... * Learn to build dynamically configurable models for hyperparameter tuning * Get hands-on experience with using Optuna to efficiently optimize hyperparameters ### Learning Resources {% file src="/files/vvBqrsC8gxAaEvrND86F" %} Presentation Slides from this week {% endfile %} ### Until next week you should... * Complete [Module 2: Working with Images using TorchVision](https://learn.deeplearning.ai/specializations/pytorch-for-deep-learning-professional-certificate/lesson/oppq8k/introduction-to-torchvision) of Course 2 (PyTorch: Techniques and Ecosystem Tools) * Complete the exercise assignment in [this notebook](https://colab.research.google.com/github/opencampus-sh/course-material/blob/main/applied-machine-learning/week-05/Week5_Notebook1_Fast-Food.ipynb) --- # 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://opencampus.gitbook.io/opencampus-machine-learning-program/courses/machine-learning-with-tensorflow/week-5-convolutional-neural-networks-part-ii.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.