Deep Learning for Dummies

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  • Format: Paperback
  • Copyright: 2019-05-14
  • Publisher: For Dummies

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Supplemental Materials

What is included with 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.  

Author Biography

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.

Table of Contents

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, Vector, and Matrix Operations 96

Creating a matrix 97

Performing matrix multiplication 99

Executing advanced matrix operations 100

Extending analysis to tensors 102

Using vectorization effectively 104

Interpreting Learning as Optimization 105

Exploring cost functions 105

Descending the error curve 106

Learning the right direction 107

Updating 109

Chapter 6: Laying Linear Regression Foundations 111

Combining Variables 112

Working through simple linear regression 112

Advancing to multiple linear regression 113

Including gradient descent 115

Seeing linear regression in action 116

Mixing Variable Types 117

Modeling the responses 117

Modeling the features 118

Dealing with complex relations 119

Switching to Probabilities 121

Specifying a binary response 121

Transforming numeric estimates into probabilities 122

Guessing the Right Features 124

Defining the outcome of incompatible features 124

Solving overfitting using selection and regularization 125

Learning One Example at a Time 127

Using gradient descent 127

Understanding how SGD is different 127

Chapter 7: Introducing Neural Networks 131

Discovering the Incredible Perceptron 132

Understanding perceptron functionality 132

Touching the nonseparability limit 134

Hitting Complexity with Neural Networks 136

Considering the neuron 136

Pushing data with feed-forward 138

Going even deeper into the rabbit hole 140

Using backpropagation to adjust learning 143

Struggling with Overfitting 146

Understanding the problem 146

Opening the black box 146

Chapter 8: Building a Basic Neural Network 149

Understanding Neural Networks 150

Defining the basic architecture 151

Documenting the essential modules 153

Solving a simple problem 155

Looking Under the Hood of Neural Networks 158

Choosing the right activation function 158

Relying on a smart optimizer 160

Setting a working learning rate 161

Chapter 9: Moving to Deep Learning 163

Seeing Data Everywhere 164

Considering the effects of structure 164

Understanding Moore’s implications 165

Considering what Moore’s Law changes 166

Discovering the Benefits of Additional Data 167

Defining the ramifications of data 168

Considering data timeliness and quality 168

Improving Processing Speed 169

Leveraging powerful hardware 170

Making other investments 170

Explaining Deep Learning Differences from Other Forms of AI 171

Adding more layers 172

Changing the activations 174

Adding regularization by dropout 175

Finding Even Smarter Solutions 176

Using online learning 176

Transferring learning 177

Learning end to end 177

Chapter 10: Explaining Convolutional Neural Networks 179

Beginning the CNN Tour with Character Recognition 180

Understanding image basics 180

Explaining How Convolutions Work 183

Understanding convolutions 183

Simplifying the use of pooling 187

Describing the LeNet architecture 188

Detecting Edges and Shapes from Images 193

Visualizing convolutions 194

Unveiling successful architectures 196

Discussing transfer learning 197

Chapter 11: Introducing Recurrent Neural Networks 201

Introducing Recurrent Networks 202

Modeling sequences using memory 202

Recognizing and translating speech 204

Placing the correct caption on pictures 206

Explaining Long Short-Term Memory 207

Defining memory differences 208

Walking through the LSTM architecture 209

Discovering interesting variants 211

Getting the necessary attention 212

Part 3: Interacting with Deep Learning 215

Chapter 12: Performing Image Classification 217

Using Image Classification Challenges 218

Delving into ImageNet and MS COCO 219

Learning the magic of data augmentation 221

Distinguishing Traffic Signs 223

Preparing image data 224

Running a classification task 228

Chapter 13: Learning Advanced CNNs 233

Distinguishing Classification Tasks 234

Performing localization 235

Classifying multiple objects 235

Annotating multiple objects in images 237

Segmenting images 237

Perceiving Objects in Their Surroundings 239

Discovering how RetinaNet works 239

Using the Keras-RetinaNet code 241

Overcoming Adversarial Attacks on Deep Learning Applications 245

Tricking pixels 246

Hacking with stickers and other artifacts 248

Chapter 14: Working on Language Processing 251

Processing Language 252

Defining understanding as tokenization 253

Putting all the documents into a bag 254

Memorizing Sequences that Matter 257

Understanding semantics by word embeddings 257

Using AI for Sentiment Analysis 261

Chapter 15: Generating Music and Visual Art 269

Learning to Imitate Art and Life 270

Transferring an artistic style 271

Reducing the problem to statistics 272

Understanding that deep learning doesn’t create 274

Mimicking an Artist 274

Defining a new piece based on a single artist 274

Combining styles to create new art 276

Visualizing how neural networks dream 276

Using a network to compose music 277

Chapter 16: Building Generative Adversarial Networks 279

Making Networks Compete 280

Finding the key in the competition 280

Achieving more realistic results 282

Considering a Growing Field 289

Inventing realistic pictures of celebrities 289

Enhancing details and image translation 290

Chapter 17: Playing with Deep Reinforcement Learning 293

Playing a Game with Neural Networks 294

Introducing reinforcement learning 294

Simulating game environments 296

Presenting Q-learning 299

Explaining Alpha-Go 302

Determining if you’re going to win 303

Applying self-learning at scale 305

Part 4: The Part of Tens 307

Chapter 18: Ten Applications that Require Deep Learning 309

Restoring Color to Black-and-White Videos and Pictures 310

Approximating Person Poses in Real Time 310

Performing Real-Time Behavior Analysis 311

Translating Languages 312

Estimating Solar Savings Potential 312

Beating People at Computer Games 313

Generating Voices 314

Predicting Demographics 314

Creating Art from Real-World Pictures 315

Forecasting Natural Catastrophes 316

Chapter 19: Ten Must-Have Deep Learning Tools 317

Compiling Math Expressions Using Theano 317

Augmenting TensorFlow Using Keras 318

Dynamically Computing Graphs with Chainer 319

Creating a MATLAB-Like Environment with Torch 319

Performing Tasks Dynamically with PyTorch 320

Accelerating Deep Learning Research Using CUDA 321

Supporting Business Needs with Deeplearning4j 323

Mining Data Using Neural Designer 323

Training Algorithms Using Microsoft Cognitive Toolkit (CNTK) 324

Exploiting Full GPU Capability Using MXNet 325

Chapter 20: Ten Types of Occupations that Use Deep Learning 327

Managing People 327

Improving Medicine 328

Developing New Devices 329

Providing Customer Support 329

Seeing Data in New Ways 330

Performing Analysis Faster 331

Creating a Better Work Environment 331

Researching Obscure or Detailed Information 333

Designing Buildings 333

Enhancing Safety 334

Index 335

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