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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
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...
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.
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...