Brief Introduction
Leverage the power of Keras to build and train state-of-the-art deep learning modelsDescription
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.
About the Author
Giuseppe Ciaburro holds a PhD in environmental technical physics and two master's degrees. His research was focused on machine learning applications in the study of the urban sound environments. He works at Built Environment Control Laboratory—Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of 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 has a degree in biology and computer science. He has worked with application development and machine learning. His recent achievement was building the biggest ever mobile machine learning course which has many different machine learning and deep learning models in Python and translated into both Android and iOS applications to incorporate some elements of machine learning into mobile application.
Requirements
- Requirements
- Sound knowledge of the Python language, machine learning, and basic familiarity with Keras library would be useful to easily grasp concepts explained in this course.
Packt Publishing
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