Keras 2.x Projects

More Information
Learn
  • Study in detail the process used to develop deep learning applications
  • Discover how optical character recognition works
  • Control the movements of a robot using Deep Q-Network (DQN)
  • Explore and apply various reinforcement learning techniques
  • Label sentences in the Reuters newswire with Keras deep neural network
  • Analyze, understand, and generate texts using Natural Language Toolkit
About

Keras is a Python library that provides a simple and clean way to create a range of deep learning models. This course introduces you to Keras and shows you how to create applications with maximum readability.

You take your first steps by getting introduced to Keras, its benefits, and its applications. As you get comfortable with Keras, you will learn how to predict business outcomes using time series data and various forecasting techniques. By learning the basic concepts of reinforcement learning, you will be able to create algorithms that can learn and adapt to environmental changes and control your robots. Then, you will learn various natural language processing techniques and use the Natural Language Toolkit to analyze, classify, and tag text.

By the end of the course, you’ll have the skills and the confidence to work on existing deep learning projects or create your own applications.

The code bundle for this course can be downloaded from here: https://github.com/TrainingByPackt/Keras-2.X-Projects-eLearning

Features
  • Study the Keras architecture and its different models in detail
  • Learn to solve time series regression issues with recurrent neural networks
  • Understand and use reinforcement learning to control a mechanical system
Course Length 5 hours 23 minutes
ISBN 9781838648909
Date Of Publication 8 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 UniversitĂ  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.

Nimish Narang

Nimish Narang has graduated from UBC with a degree in biology and computer science in 2016. He has developed Mobile apps for Android and iOS since 2015. He is focused on data analysis and machine learning from the past two years and has previously published Keras and Professional Scala with Packt.