Deep Machine Learning: A Comprehensive Beginner Developer Guide to Deep Machine Learning Algorithms, Concepts and Techniques

Joe Grant

ISBN 10: 1088209696 / ISBN 13: 9781088209691

Verlag: IngramSpark, 2023

Beschreibung

This item is printed on demand. New copy - Usually dispatched within 5-9 working days 195.

Bestandsnummer des Verkäufers C9781088209691

Bibliografische Details

• Titel: Deep Machine Learning: A Comprehensive Beginner Developer Guide to Deep Machine Learning Algorithms, Concepts and Techniques
• Verlag: IngramSpark
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1088209696
• ISBN-13: 9781088209691
• Einband: Paperback / softback
• Zustand: New

Über diesen Titel

Inhaltsangabe

Are you interested in Machine Learning? Are you fascinated by how robots work? Are you ready to open up to the dynamics of technological change?

Machine Learning has been approached in a definitive manner as a subset falling under a larger set of Artificial intelligence. It majorly focuses on the aspect of learning of machines basing on the experience and predicting consequences and actions of the machines that revolve around their experience in the past.

The field has made it easy for computers and machines to enact decisions that are data driven instead of explicit programming with regard to a particular task. The algorithms and programs are designed to enable machines and computers to learn by themselves. With time, they get to improve when there is an introduction of new and unique data. The learning process applies the use of training data that sustains the coming up of a model. Insertion of new data brings up predictions that are based on the model. This means that machines are given the capability to foretell on their own.

The predictions are then examined closely to identify their accuracy. If accuracy receives positive feedback, then the machine learning algorithm is trained over and over again through the assistance of a data training augmented set.

Machine learning tasks are broken into various wider categories. Supervised learning aims at coming up with a model that is mathematics of a data set with desired inputs and outputs. Semi-supervised learning aims at coming up with mathematical models from incomplete data training. You will realize that sample inputs miss expected/desired output in such a case.

This book will help you understand more about Deep Machine Learning. In the pages of this book, you will be able to get important chapters that include:

·      History of Machine Learning

·      The Benefits

·      The Challenges you may Encounter

·      Applications of Machine Learning

·      Artificial Intelligence

·      Big Data

·      And much more!

With such knowledge, you will be able to embrace technological advancements and be ready for the future.