Deep Learning for Beginners A comprehensive introduction of deep learning fundamentals for beginners to understanding frameworks, neural networks, large datasets, and creative applications with ease

Steven Cooper

ISBN 10: 3903331074 / ISBN 13: 9783903331075

Published by Data Science, 2019

About this Item

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Bibliographic Details

• Title: Deep Learning for Beginners A comprehensive introduction of deep learning fundamentals for beginners to understanding frameworks, neural networks, large datasets, and creative applications with ease
• Publisher: Data Science
• Publication Date: 2019
• Language: English
• ISBN 10: 3903331074
• ISBN 13: 9783903331075
• Binding: PAP
• Condition: New

About this title

Synopsis

If you are looking for a complete beginners guide to learn deep learning with examples, in just a few hours, then you need to continue reading.

This book delves into the basics of deep learning for those who are enthusiasts concerning all things machine learning and artificial intelligence. For those who have seen movies which show computer systems taking over the world like, Terminator, or benevolent systems that watch over the population, i.e. Person of Interest, this should be right up your alley.

This book will give you the basics of what deep learning entails. That means frameworks used by coders and significant components and tools used in deep learning, that enable facial recognition, speech recognition, and virtual assistance. Yes, deep learning provides the tools through which systems like Siri became possible.

Grab your copy today and learn:

• Deep learning utilizes frameworks which allow people to develop tools which are able to offer better abstraction, along with simplification of hard programming issues. TensorFlow is the most popular tool and is used by corporate giants such as Airbus, Twitter, and even Google.
• The book illustrates TensorFlow and Caffe2 as the prime frameworks that are used for development by Google and Facebook. Facebook illustrates Caffe2 as one of the lightweight and modular deep learning frameworks, though TensorFlow is the most popular one, considering it has a lot of popularity, and thus, a big forum, which allows for assistance on main problems.
• The book considers several components and tools of deep learning such as the neural networks; CNNs, RNNs, GANs, and auto-encoders. These algorithms create the building blocks which propel deep learning and advance it.
• The book also considers several applications, including chatbots and virtual assistants, which have become the main focus for deep learning into the future, as they represent the next frontier in information gathering and connectivity. The Internet of Things is also represented here, as deep learning allows for integration of various systems via an artificial intelligence system, which is already being used for the home and car functions.
• And much more...

The use of data science adds a lot of value to businesses, and we will continue to see the need for data scientists grow.

This book is probably one of the best books for beginners. It's a step-by-step guide for any person who wants to start learning deep learning and artificial intelligence from scratch.

When data science can reduce spending costs by billions of dollars in our economy, why wait to jump in?