This repository contains the project files and resources for the Coursera project "Hyperparameter Tuning with Keras Tuner." The project focuses on exploring and implementing hyperparameter tuning techniques using the Keras Tuner library.
Course link: https://www.coursera.org/learn/keras-tuner/home/week/1
Certificate: Certificate.pdf
The project aims to provide hands-on experience with hyperparameter tuning, an essential aspect of optimizing machine learning models. By leveraging Keras Tuner, participants will learn how to efficiently search and select the best hyperparameters for their neural network models. The project covers various hyperparameter tuning techniques, such as random search, grid search, and Bayesian optimization.
The repository is organized as follows:
README.md: The file you are currently reading, providing an overview of the repository.notebooks/: This directory contains Jupyter notebooks used for the project, including step-by-step instructions, code examples, and exercises.data/: (Optional) If applicable, this folder holds any sample datasets or data files used in the project.code/: (Optional) If applicable, this directory contains additional code files or scripts used in the project.
To complete the project and work with the provided notebooks, you will need the following:
- Python 3.x
- Jupyter Notebook or JupyterLab
- Keras Tuner library (installation instructions provided in the notebooks)
- TensorFlow (version specified in the notebooks)
Feel free to explore the project materials, including the Jupyter notebooks in the notebooks/ directory. The notebooks will guide you through the steps of hyperparameter tuning using Keras Tuner, providing explanations, code snippets, and practical exercises. Start by opening the introductory notebook and follow the instructions to proceed.
If you encounter any issues, have questions, or would like to share feedback, please feel free to raise an issue on this repository.
Specify the license under which the project materials are released. MIT.
Use this section to acknowledge any individuals, resources, or references that contributed to the project. Give credit to any datasets, libraries, or tutorials that were used or referenced.