Artificial Neural Network for Regression
Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
In this course, AI expert Hadelin de Ponteves guides you through a case study that shows you how to build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant.
The objective is to create a data model that predicts the net hourly electrical energy output (EP) of the plant using available hourly average ambient variables.
Go hands on with Hadelin in solving this complex, real world Deep Learning challenge that covers everything from data preprocessing to building and training an ANN, while utilizing the Machine Learning library, Tensorflow 2.0, and Google Colab, the free, browser based notebook environment that runs completely in the cloud. It is a game changing interface that will supercharge your Machine Learning toolkit.
SKILLS YOU WILL GAIN
- Importing the dataset And Splitting the dataset into the training set and test set
- Initializing the ANN ,Adding the input layer and the first hidden layer,Adding the output layer,Compiling the ANN
- Training the ANN model on the training set ,Predicting the results of the test set
WHAT YOU WILL LEARN
- Machine Learning
- Deep Learning