Practical Projects with Keras 2.X

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  • 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

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.

  • 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


Barbora stetinova

Barbora Stetinova works in an Automotive industry earned experience in data science and machine learning, leading small team, leading strategical projects and in controlling topics for 13 years. Since Sept 2018 she is a member of IT department participating on the Data science implementation in an automotive company.

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.