Artificial Neural Network for Regression
- Deep Learning Basics
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.
Who this course is for:
- Anyone interested in Machine Learning and Deep Learning
- 09:19Getting started
- 00:11Dataset + Code + Colab Link
- 03:15Importing the libraries
Hadelin is the co-founder and CEO at BlueLife AI, which leverages the power of cutting edge Artificial Intelligence to empower businesses to make massive profits by innovating, automating processes and maximizing efficiency. Hadelin is also an online entrepreneur who has created 70+ top-rated educational e-courses to the world on topics such as Machine Learning, Deep Learning, Artificial Intelligence and Blockchain, which have reached 1M+ students in 210 countries.
We are the SuperDataScience Social team. You will be hearing from us when new SDS courses are released, when we publish new podcasts, blogs, share cheatsheets and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
The Real People at SuperDataScience