Keras and Tensorflow with Grid Search Cross Validation | IRIS Data

Keras and TensorFlow are two powerful libraries that are used for building and training machine learning models. Keras is a high-level neural networks API, written in Python, that runs on top of TensorFlow. It is designed to make it easy to build and train neural networks.

Grid Search Cross Validation is a method used to find the best set of parameters for a machine learning model. It works by testing different combinations of parameters and evaluating their performance. The best combination of parameters is then chosen for the final model.

In this example, we will be using the IRIS dataset, which contains information about different variations of the IRIS flower, such as the setosa, virginica, and versicolor. We will use Keras and TensorFlow to build a neural network model and use Grid Search Cross Validation to find the best set of parameters for the model.

First, we will preprocess the IRIS data and split it into training and testing sets. Next, we will use Keras to define the neural network architecture and TensorFlow to train the model. We will then use Grid Search Cross Validation to test different combinations of parameters, such as the number of hidden layers and the number of neurons in each layer, and evaluate their performance.

After the model is trained and the best set of parameters is found, we can use it to classify new IRIS flowers based on their characteristics.

In summary, The Keras and Tensorflow with Grid Search Cross Validation using IRIS Data is a powerful combination of libraries and techniques that can be used to classify the IRIS flowers. Keras and Tensorflow are used to build and train a neural network and Grid Search Cross Validation is used to fine-tune the best set of parameters for the model.

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in Python programming: Keras and Tensorflow with Grid Search Cross Validation.

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