Deep Learning For Dummies

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.

Autorentext

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.

Klappentext

• Learn how deep learning is an essential technology
• Experiment with deep learning in a Python environment

• See examples of major deep learning application types Dive deep into deep learning Deep learning provides the means for discerning patterns in the data that drive online business, medicine, research, social media outlets, and many elements of daily life. This book gives you the information you need to take the mystery out of the topicand 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. Inside...

• Create examples using TensorFlow and Keras
• Benefit from hands-on activities to make learning easier
• Use deep learning more effectively with the right tools
• Make sense of complex algorithms

Zusammenfassung
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 topicand 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.

Inhalt

Introduction 1

About This Book 1

Foolish Assumptions 2

Icons Used in This Book 3

Beyond the Book 4

Where to Go from Here 5

Part 1: Discovering Deep Learning 7

Chapter 1: Introducing Deep Learning 9

Defining What Deep Learning Means 10

Starting from Artificial Intelligence 10

Considering the role of AI 12

Focusing on machine learning 15

Moving from machine learning to deep learning 16

Using Deep Learning in the Real World 18

Understanding the concept of learning 18

Performing deep learning tasks 19

Employing deep learning in applications 19

Considering the Deep Learning Programming Environment 19

Overcoming Deep Learning Hype 22

Discovering the start-up ecosystem 22

Knowing when not to use deep learning 22

Chapter 2: Introducing the Machine Learning Principles 25

Defining Machine Learning 26

Understanding how machine learning works 26

Understanding that it's pure math 27

Learning by different strategies 28

Training, validating, and testing data 30

Looking for generalization 31

Getting to know the limits of bias 32

Keeping model complexity in mind 33

Considering the Many Different Roads to Learning 33

Understanding there is no free lunch 34

Discovering the five main approaches 34

Delving into some different approaches 36

Awaiting the next breakthrough 40

Pondering the True Uses of Machine Learning 40

Understanding machine learning benefits 41

Discovering machine learning limits 43

Chapter 3: Getting and Using Python 45

Working with Python in this Book 46

Obtaining Your Copy of Anaconda 46

Getting Continuum Analytics Anaconda 47

Installing Anaconda on Linux 47

Installing Anaconda on MacOS 48

Installing Anaconda on Windows 49

Downloading the Datasets and Example Code 54

Using Jupyter Notebook 54

Defining the code repository 56

Getting and using datasets 61

Creating the Application 62

Understanding cells 62

Adding documentation cells 63

Using other cell types 64

Understanding the Use of Indentation 65

Adding Comments 66

Understanding comments 67

Using comments to leave yourself reminders 68

Using comments to keep code from executing 69

Getting Help with the Python Language 69

Working in the Cloud 70

Using the Kaggle datasets and kernels 70

Using the Google Colaboratory 70

Chapter 4: Leveraging a Deep Learning Framework 73

Presenting Frameworks 74

Defining the differences 74

Explaining the popularity of frameworks 75

Defining the deep learning framework 77

Choosing a particular framework 78

Working with Low-End Frameworks 79

Caffe2 79

Chainer 80

PyTorch 80

MXNet 81

Microsoft Cognitive Toolkit/CNTK 82

Understanding TensorFlow 82

Grasping why TensorFlow is so good 82

Making TensorFlow easier by using TFLearn 84

Using Keras as the best simplifier 85

Getting your copy of TensorFlow and Keras 86

Fixing the C++ build tools error in Windows 88

Accessing your new environment in Notebook 89

Part 2: Considering Deep Learning Basics 91

Chapter 5: Reviewing Matrix Math and Optimization 93

Revealing the Math You Really Need 94

Working with data 94

Creating and operating with a matrix 95 Understanding Scalar, Vecto...