Mastering Deep Learning with Python

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.