
Mastering Deep Learning with Python
Design and implement next-generation AI solutions using Keras and TensorFlow
John Hearty(Author)
Packt Publishing
Published on 30. November 2018
Book
Paperback/Softback
505 pages
978-1-78899-610-5 (ISBN)
Description
Excel Deep Learning techniques, architectures, and solutions for high performance and more reliable applications
About This Book
* Learn to build faster and accurate deep learning architectures
*Work on Deep Learning models with practical examples and techniques
*Uncover the future of Deep Learning in cyber security, Blockchain, and IoT
Who This Book Is For
If you want to master Deep Learning and build Deep Learning projects on your own, then this is the book for you.
It will also appeal to you if you're a Python developer looking to expand your career in Deep Learning or if you're a Deep Learning user and want to build skills in Python programming. You're expected to have basic experience in Python programming.
What You Will Learn
* Get acquainted with the major technology trends driving Deep Learning
*Develop your intuition when choosing deep neural net models
*Learn L1, L2, and dropout regularization along with batch normalization
*Set up, train, develop, and test sets to analyze bias and variance
*Build a successful DL project and compare it to human performance
*Diagnose errors in a system and choose the best method to reduce them
In Detail
Deep Learning, inspired by the brain's ability to learn, is the next big thing scientists are adopting to develop predictive capabilities in applications. This book is a collection of newly evolved Deep Learning models, core statistical concepts, and various tools and libraries required for the implementation of deep learning models across different areas of application.
Mastering Deep Learning with Python begins by helping you choose a tool to implement and scale models, set up the development environment, and refresh the core mathematical concepts required to understand next-generation deep learning models. You'll understand how deep learning can mirror a human brain's instincts with nearly the same speed and precision in applications such as computer vision, speech recognition, natural language understanding, and cyber threat detection. Every section in this book follows a standard approach of how to choose a cutting-edge technique, perform cognitive functions, and design models to solve unique problems in a variety of business cases. Finally, you'll also learn about a range of design and deployment options for ML solutions and processes.
By the end of this book, you'll have gained mastery over Deep Learning and evolutionary approaches for monitoring and management of Deep Learning models.
About This Book
* Learn to build faster and accurate deep learning architectures
*Work on Deep Learning models with practical examples and techniques
*Uncover the future of Deep Learning in cyber security, Blockchain, and IoT
Who This Book Is For
If you want to master Deep Learning and build Deep Learning projects on your own, then this is the book for you.
It will also appeal to you if you're a Python developer looking to expand your career in Deep Learning or if you're a Deep Learning user and want to build skills in Python programming. You're expected to have basic experience in Python programming.
What You Will Learn
* Get acquainted with the major technology trends driving Deep Learning
*Develop your intuition when choosing deep neural net models
*Learn L1, L2, and dropout regularization along with batch normalization
*Set up, train, develop, and test sets to analyze bias and variance
*Build a successful DL project and compare it to human performance
*Diagnose errors in a system and choose the best method to reduce them
In Detail
Deep Learning, inspired by the brain's ability to learn, is the next big thing scientists are adopting to develop predictive capabilities in applications. This book is a collection of newly evolved Deep Learning models, core statistical concepts, and various tools and libraries required for the implementation of deep learning models across different areas of application.
Mastering Deep Learning with Python begins by helping you choose a tool to implement and scale models, set up the development environment, and refresh the core mathematical concepts required to understand next-generation deep learning models. You'll understand how deep learning can mirror a human brain's instincts with nearly the same speed and precision in applications such as computer vision, speech recognition, natural language understanding, and cyber threat detection. Every section in this book follows a standard approach of how to choose a cutting-edge technique, perform cognitive functions, and design models to solve unique problems in a variety of business cases. Finally, you'll also learn about a range of design and deployment options for ML solutions and processes.
By the end of this book, you'll have gained mastery over Deep Learning and evolutionary approaches for monitoring and management of Deep Learning models.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-78899-610-5 (9781788996105)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Person
John Hearty is a Manager of Data Science team with substantial expertise in data science and infrastructure engineering. Having started out in mobile gaming, he was drawn to the challenge of AAA console analytics. Keen to start putting advanced machine learning techniques into practice, he signed on with Microsoft to develop player modelling capabilities and big data infrastructure at an Xbox studio. His team made significant strides in engineering and data science that were replicated across Microsoft Studios. Some of the more rewarding initiatives he led included player skill modelling in asymmetrical games, and the creation of player segmentation models for individualized game experiences. Eventually, John struck out on his own as a consultant offering a comprehensive infrastructure and analytics solutions for international client teams seeking new insights or data-driven capabilities. His favorite current engagement involves creating predictive models and quantifying the importance of user connections for a popular social network. After years spent working with data, John is largely unable to stop asking questions. In his own time, he routinely builds ML solutions in Python to fulfill a broad set of personal interests. These include a novel variant on the StyleNet computational creativity algorithm and solutions for algo-trading and geolocation-based recommendation