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

François Duval

  • 出版商: W. W. Norton
  • 出版日期: 2018-01-10
  • 售價: $850
  • 貴賓價: 9.5$808
  • 語言: 英文
  • 頁數: 104
  • 裝訂: Paperback
  • ISBN: 1984050621
  • ISBN-13: 9781984050625
  • 相關分類: DeepLearningData Science
  • 無法訂購

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

商品描述(中文翻譯)

從亞馬遜購買印刷書的客戶可以免費獲得 Kindle 電子書

你是否想更深入了解沒有數學的深度學習?本書以通俗易懂的方式介紹深度學習和神經網絡及其算法。每個算法都以非常簡單的方式解釋,以便更容易理解。

本書不使用繁難的數學公式,而是包含了多個圖表和圖像,詳細介紹了所有算法及其在現實生活中的應用。

為什麼這本書與眾不同?本書將幫助您了解深度學習的本質,並教您為什麼它如此革命性和迷人。每章都會向讀者介紹深度學習算法的概念、技術和應用,並提供實際案例和實例來進行練習。

本書採用了一種不同的方法,即通過提供深度學習算法如何工作的簡單示例,並逐步構建這些示例,以涵蓋算法的更複雜部分。

目標讀者:本書適用於各種目標讀者,最適合的讀者包括:
- 想接觸深度學習,但對於複雜的數學感到害怕的初學者
- 電腦科學技術和深度學習的新手
- 數據科學和社會科學專業人士
- 教授、講師或導師,希望找到更好的方法來向學生解釋內容,使其更簡單易懂
- 專注於神經網絡和深度學習的學生和學者

本書內容包括:
- 深度學習:什麼是深度學習?為什麼要學習深度學習?
- 深度學習的先決條件
- 人工神經網絡:什麼是人工神經網絡?為什麼要使用人工神經網絡?
- 深度學習的一般介紹
- 多層感知器和反向傳播:它們是如何工作的?
- 卷積神經網絡(CNN):它是如何工作的?
- 其他深度學習算法
- 深度學習的應用
- 深度學習應用的未來展望
- 深度學習的長期願景