Deep Learning For Dummies

Deep Learning For Dummies (For Dummies (Computer/Tech))
By 作者: John Paul Mueller - Luca Massaron
ISBN-10 书号: 1119543045
ISBN-13 书号: 9781119543046
Edition 版本: 1
Release Finelybook 出版日期: 2019-05-14
pages 页数: (368 )

The Book Description

About the Author
John Paul Mueller is the author of over 100 books including AI for Dummies, Python for Data Science for Dummies, Machine Learning for Dummies, and Algorithms for Dummies.
Luca Massaron is a data scientist who interprets big data and transforms it into smart data by means of the simplest and most effective data mining and machine learning techniques. He is a Google Developer Expert (GDE) in machine learning.

About this book
Take a deep dive into deep learning
Deep learning provides the means for discerning patterns in the data that drive online business and social media outlets. Deep Learning for Dummies gives you the information you need to take the mystery out of the topic—and all of the underlying technologies associated with it.
In no time, you’ll make sense of those increasingly confusing algorithms, and find a simple and safe environment to experiment with deep learning. The book develops a sense of precisely what deep learning can do at a high level and then provides examples of the major deep learning application types.
Includes sample code
Provides real-world examples within the approachable text
Offers hands-on activities to make learning easier
Shows you how to use Deep Learning more effectively with the right tools
This book is perfect for those who want to better understand the basis of the underlying technologies that we use each and every day.

Brief contents
Introduction. 1
Part 1: Discovering Deep Learning. 7
CHAPTER 1: Introducing Deep Learning. 9
CHAPTER 2: Introducing the Machine Learning Principles . 25
CHAPTER 3: Getting and Using Python. 45
CHAPTER 4: Leveraging a Deep Learning Framework. 73
Part 2: Considering Deep Learning Basics. 91
CHAPTER 5: Reviewing Matrix Math and Optimization . 93
CHAPTER 6: Laying Linear Regression Foundations. 111
CHAPTER 7: Introducing Neural Networks. 131
CHAPTER 8: Building a Basic Neural Network. 149
CHAPTER 9: Moving to Deep Learning . 163
CHAPTER 10: Explaining Convolutional Neural Networks. 179
CHAPTER 11: Introducing Recurrent Neural Networks . 201
Part 3: Interacting with Deep Learning. 215
CHAPTER 12: Performing Image Classification . 217
CHAPTER 13: Learning Advanced CNNs. 233
CHAPTER 14: Working on Language Processing. 251
CHAPTER 15: Generating Music and Visual Art. 269
CHAPTER 16: Building Generative Adversarial Networks. 279
CHAPTER 17: Playing with Deep Reinforcement Learning. 293
Part 4: The Part of Tens. 307
CHAPTER 18: Ten Applications that Require Deep Learning. 309
CHAPTER 19: Ten Must-Have Deep Learning Tools . 317
CHAPTER 20: Ten Types of Occupations that Use Deep Learning. 327
Index. 335


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