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Courses>Data Science>Artificial Neural Network for Regression
DevelopmentArtificial Neural Network for Regression
Price:Free
Length:1 hour
Content type:video
level:beginner
Updated:20 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Data Science>Artificial Neural Network for Regression
Artificial Neural Network for Regression
4.7 (41.7k)
1 hour
41699 students
What you will learn
1How to implement an Artificial Neural Network in Python
2How to do Regression
3How to use Google Colab
Target audiences
1Anyone interested in Machine Learning and Deep Learning
Requirements
1Deep Learning Basics
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
Description

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.


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Make the most out of your online education
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All rights reserved
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Careertail
Courses>Data Science>Artificial Neural Network for Regression
DevelopmentArtificial Neural Network for Regression
Price:Free
Length:1 hour
Content type:video
level:beginner
Updated:20 February 2024
Published:21 August 2022
Similar courses
Opportunities
Courses>Data Science>Artificial Neural Network for Regression
Artificial Neural Network for Regression
4.7 (41.7k)
1 hour
41699 students
What you will learn
1How to implement an Artificial Neural Network in Python
2How to do Regression
3How to use Google Colab
Target audiences
1Anyone interested in Machine Learning and Deep Learning
Requirements
1Deep Learning Basics
FAQ
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
Description

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.


Similar courses
Opportunities
Make the most out of your online education
Careertail
Copyright © 2021 Careertail.
All rights reserved
Quick Links
Get StartedLog InAbout UsCourses
Company
BlogContactsPrivacy PolicyCookie PolicyTerms and Conditions
Stay up to date
Trustpilot