If you are a Scala, Java, or Python developer with an interest in machine learning and data analysis and are eager to learn how to apply common machine learning techniques at scale using the Spark framework, this is the book for you. While it may be useful to have a basic understanding of Spark, no previous experience is required.
Ideal for programmers, data scientists, and AI engineers, this book includes an overview of the machine learning process and walks you through classification with structured data.
Provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks
Learning from Data: A Short Course
Provides a practical guide to get started and execute on machine learning within a few days without necessarily knowing much about machine learning.The first five chapters are enough to get you started and the next few chapters provide you ...
Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
The math content is kept to a minimum to focus on what matters-applying the concepts in useful contexts. This book is sure to benefit anyone curious about the fascinating field of machine learning.
By the time you reach the end of this book, you will have become a Keras expert and will be able to apply deep learning in your own projects.
This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods.
In The Voice in the Machine, Roberto Pieraccini examines six decades of work in science and technology to develop computers that can interact with humans using speech and the industry that has arisen around the quest for these technologies.
The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance.