Deep Learning for the Layman: Visual Guide without Maths added (Data Sciences)

François Duval

  • 出版商: CreateSpace Independent Publishing Platform
  • 出版日期: 2018-01-10
  • 售價: $753
  • 貴賓價: 9.5$715
  • 語言: 英文
  • 頁數: 104
  • 裝訂: Paperback
  • ISBN: 1984050621
  • ISBN-13: 9781984050625
  • 相關分類: DeepLearning 深度學習資料科學

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商品描述

Free Kindle eBook for customers who purchase the print book from Amazon


Are you thinking of learning more about Deep Learning without Maths?

This book has been written in layman's terms as an introduction to deep learning and neural networks and their algorithms. Each algorithm is explained very easily for more understanding.

 Several Visual Illustrations and Examples

Instead of tough math formulas, this book contains several graphs and images which detail all algorithms and their applications in all area of the real life.

 Why this book is different ?

This book will help you explore exactly what deep learning is and will also teach you about why it is so revolutionary and fascinating. The chapters will introduce the reader to the concepts, techniques, and applications of deep learning algorithms with the practical case studies and walk-through examples on which to practice.

This book takes a different approach that is based on providing simple examples of how deep learning algorithms work, and building on those examples step by step to encompass the more complicated parts of the algorithms. 

Target Users

The book designed for a variety of target audiences. The most suitable users would include: 
  • Beginners who want to approach deep learning, but are too afraid of complex math to start

  • Newbies in computer science techniques and deep learning
  • Professionals in data science and social sciences
  • Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way 
  • Students and academicians, especially those focusing on neural networks and deep learning

What’s inside this book?

  • Deep Learning: What & Why?
  • Pre-requisite for Deep Learning
  • Artificial Neural Networks: what and why?
  • General Presentation of Deep Learning
  • Multilayer Perceptron and Backpropagation: How they are work?
  • Convolutional Neural Networks (CNN): How it is works?
  • Other Deep Learning Algorithms
  • Deep Learning Applications
  • Our Future with Deep Learning Applied
  • The Long-Term Vision of Deep Learning