Practical Projects with Keras 2.X

More Information
Learn
  • Apply regression methods to your data and understand how the regression algorithm works
  • Import and organize data for neural network classification analysis
  • Solve a regression problem through the least squares algorithm
  • Use a classification algorithm to predict the outcome of an event
  • Train, test, and deploy a model in Keras environment
  • Implement multilayer neural networks in Keras
  • Improve the performance of a model by removing outliers
About

Keras is a user-friendly, modular, and intuitive neural network library that enables you to experiment with deep neural networks.

Practical Projects with Keras 2.x explains how to leverage the power of Keras to build and train state-of-the-art deep learning models through a series of practical projects that look at a range of real-world application areas. You'll begin by exploring concepts underlying regression, such as the differences between simple and multiple regression and algebraically representing a multiple linear regression problem. Moving on, you'll discover various classification techniques, such as Naive Bayes and Mixture Gaussian, and use these to solve practical problems. The course ends by teaching you the basic concepts of multilayer neural networks and how to implement them in Keras environment.

By the end of this course, you will have the knowledge you need to train your own deep learning models to solve different kinds of problems.

Features
  • Learn in detail the different types of regression techniques
  • Understand various classification methods and implement them in a Keras environment
  • Learn about multi-layer neural networks and discover ways to use them in a Keras environment
Course Length 2 hours 47 minutes
ISBN 9781838827236
Date Of Publication 25 Apr 2019

Authors

Giuseppe Ciaburro

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Università degli Studi della Campania Luigi Vanvitelli, Italy. He has over 15 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

Barbora Stetinova

For 13 years working in Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics.

Since Sept 2018 as a member of IT department participating on the Data science implementation in an automotive company.

In parallel, since Aug 2017, engaged in strategical group projects for the automotive company and with side contract as an analytical external consultant for different industries (retail, sensorics, building) at Leadership Synergy Community.

Data science trainer for Elderberry data, specialized in MS Excel and Knime analytics platform in both face-to-face and elearning forms.

Currently working on elearning course Python with Keras for PACKT publishing.

I am motivated by learning new things, achieving goals and helping others.