bestcourses is supported by learners. When you buy through links on our website, we may earn an affiliate commission. Learn more
Artificial Neural Network for Regression
Build an ANN Regression model to predict the electrical energy output of a Combined Cycle Power Plant
Created by Hadelin de Ponteves, offered on Udemy
To make sure that we score courses properly, we pay a lot of attention to the reviews students leave on courses and how many students are taking a course in the first place. This course has a total of 39987 students which left 3875 reviews at an average rating of 4.6. Impressive!
We analyze course length to see if courses cover all important aspects of a topic, taking into account how long the course is compared to the category average. This course has a length of 1 hours 13 minutes, which is pretty short. This might not be a bad thing, but we've found that longer courses are often more detailed & comprehensive. The average course length for this entire category is 7 hours 54 minutes.
This course currently has a bestcourses score of 8.6/10, which makes it a great course to learn from. On our entire platform, only 15% of courses achieve this rating!
Are you ready to flex your Deep Learning skills by learning how to build and implement an Artificial Neural Network using Python from scratch?
Testing your skills with practical courses is one of the best and most enjoyable ways to learn data science…and now we’re giving you that chance for FREE.
In this free 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’s a game-changing interface that will supercharge your Machine Learning toolkit.
Check out what’s in store for you when you enroll:
Part 1: Data Preprocessing
Importing the dataset
Splitting the dataset into the training set and test set
Part 2: Building an ANN
Initializing the ANN
Adding the input layer and the first hidden layer
Adding the output layer
Compiling the ANN
Part 3: Training the ANN
Training the ANN model on the training set
Predicting the results of the test set
More about Combined-Cycle Power Plants
A combined-cycle power plant is an electrical power plant in which a Gas Turbine (GT) and a Steam Turbine (ST) are used in combination to produce more electrical energy from the same fuel than that would be possible from a single cycle power plant.
The gas turbine compresses air and mixes it with a fuel heated to a very high temperature. The hot air-fuel mixture moves through the blades, making them spin. The fast-spinning gas turbine drives a generator to generate electricity. The exhaust (waste) heat escaped through the exhaust stack of the gas turbine is utilized by a Heat Recovery Steam Generator (HSRG) system to produce steam that spins a steam turbine. This steam turbine drives a generator to produce additional electricity. CCCP is assumed to produce 50% more energy than a single power plant.
What you will learn
- How to implement an Artificial Neural Network in Python
- How to do Regression
- How to use Google Colab
- Deep Learning Basics