Get yourself trained on Practical Projects with with this Online Training Practical Projects with Keras 2.X.
Online Training Practical Projects with Keras 2.X
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.About the AuthorGiuseppe Ciaburro holds a Ph.D. 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 Universit degli Studi della Campania Luigi Vanvitelli, Italy. He has an experience of over 15 years 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 StetinovaFor 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.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.