Produktbild: Programming Neural Networks with Python

Programming Neural Networks with Python

61,70 €

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.06.2025

Verlag

Rheinwerk Publishing

Seitenzahl

457

Maße (L/B/H)

25,2/17,5/1,8 cm

Gewicht

739 g

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2696-2

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

06.06.2025

Verlag

Rheinwerk Publishing

Seitenzahl

457

Maße (L/B/H)

25,2/17,5/1,8 cm

Gewicht

739 g

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2696-2

Herstelleradresse

Rheinwerk Verlag GmbH
Rheinwerkallee 4
53227 Bonn
DE

Email: service@rheinwerk-verlag.de

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  • Produktbild: Programming Neural Networks with Python
  • ... Preface ... 15

    1 ... Introduction ... 17

    1.1 ... Why Neural Networks? ... 17

    1.2 ... About This Book ... 18

    1.3 ... The Contents in Brief ... 19

    1.4 ... Is This Bee a Queen Bee? ... 22

    1.5 ... An Artificial Neural Network for the Bee Colony ... 23

    1.6 ... From Biology to the Artificial Neuron ... 28

    1.7 ... Classification and the Rest ... 32

    1.8 ... Summary ... 39

    1.9 ... Further Reading ... 39

    PART I ... Up and Running ... 41

    2 ... Starter Kit for Developing Neural Networks with Python ... 43

    2.1 ... The Technical Development Environment ... 43

    2.2 ... Summary ... 63

    3 ... A Simple Neural Network ... 65

    3.1 ... Background ... 65

    3.2 ... Bring on the Neural Network! ... 65

    3.3 ... Neuron Zoom-In ... 68

    3.4 ... Step Function ... 73

    3.5 ... Perceptron ... 75

    3.6 ... Points in Space: Vector Representation ... 76

    3.7 ... Horizontal and Vertical: Column and Line Notation ... 82

    3.8 ... The Weighted Sum ... 84

    3.9 ... Step-by-Step: Step Functions ... 85

    3.10 ... The Weighted Sum Reloaded ... 85

    3.11 ... All Together ... 86

    3.12 ... Task: Robot Protection ... 89

    3.13 ... Summary ... 91

    3.14 ... Further Reading ... 91

    4 ... Learning in a Simple Network ... 93

    4.1 ... Background: Plans Are Being Made ... 93

    4.2 ... Learning in Python Code ... 94

    4.3 ... Perceptron Learning ... 94

    4.4 ... Separating Line for a Learning Step ... 98

    4.5 ... Perceptron Learning Algorithm ... 99

    4.6 ... The Separating Lines or Hyperplanes for the Example ... 103

    4.7 ... scikit-learn Compatible Estimator ... 106

    4.8 ... scikit-learn Perceptron Estimator ... 113

    4.9 ... Adaline ... 115

    4.10 ... Summary ... 125

    4.11 ... Further Reading ... 126

    5 ... Multilayer Neural Networks ... 127

    5.1 ... A Real Problem ... 127

    5.2 ... Solving XOR ... 129

    5.3 ... Preparations for the Launch ... 134

    5.4 ... The Plan for Implementation ... 135

    5.5 ... The Setup ("class") ... 136

    5.6 ... The Initialization ("__init__") ... 138

    5.7 ... Something for In-Between ("print") ... 141

    5.8 ... The Analysis ("predict") ... 141

    5.9 ... The Usage ... 143

    5.10 ... Summary ... 145

    6 ... Learning in a Multilayer Network ... 147

    6.1 ... How Do You Measure an Error? ... 147

    6.2 ... Gradient Descent: An Example ... 149

    6.3 ... A Network of Sigmoid Neurons ... 157

    6.4 ... The Cool Algorithm with Forward Delta and Backpropagation ... 158

    6.5 ... A “fit” Run ... 170

    6.6 ... Summary ... 178

    6.7 ... Further Reading ... 178

    7 ... Examples of Deep Neural Networks ... 179

    7.1 ... Convolutional Neural Networks ... 179

    7.2 ... Transformer Neural Networks ... 194

    7.3 ... The Optimization Method ... 204

    7.4 ... Preventing Overfitting ... 205

    7.5 ... Summary ... 207

    7.6 ... Further Reading ... 207

    8 ... Programming Deep Neural Networks Using TensorFlow 2 ... 209

    8.1 ... Convolutional Networks for Handwriting Recognition ... 209

    8.2 ... Transfer Learning with Convolutional Neural Networks ... 223

    8.3 ... Transfer Learning with Transformer Neural Networks ... 231

    8.4 ... Summary ... 236

    8.5 ... Further Reading ... 236

    PART II ... Deep Dive ... 239

    9 ... From Brain to Network ... 241

    9.1 ... Your Brain in Action ... 241

    9.2 ... The Nervous System ... 242

    9.3 ... The Brain ... 243

    9.4 ... Neurons and Glial Cells ... 245

    9.5 ... A Transfer in Detail ... 247

    9.6 ... Representation of Cells and Networks ... 249

    9.7 ... Summary ... 251

    9.8 ... Further Reading ... 251

    10 ... The Evolution of Artificial Neural Networks ... 253

    10.1 ... The 1940s ... 254

    10.2 ... The 1950s ... 255

    10.3 ... The 1960s ... 257

    10.4 ... The 1970s ... 257

    10.5 ... The 1980s ... 258

    10.6 ... The 1990s ... 270

    10.7 ... The 2000s ... 271

    10.8 ... The 2010s ... 272

    10.9 ... Summary ... 274

    10.10 ... Further Reading ... 274

    11 ... The Machine Learning Process ... 277

    11.1 ... The CRISP-DM Model ... 277

    11.2 ... Ethical and Legal Aspects ... 281

    11.3 ... Feature Engineering ... 290

    11.4 ... Summary ... 317

    11.5 ... Further Reading ... 318

    12 ... Learning Methods ... 319

    12.1 ... Learning Strategies ... 319

    12.2 ... Tools ... 345

    12.3 ... Summary ... 350

    12.4 ... Further Reading ... 350

    13 ... Areas of Application and Real-Life Examples ... 351

    13.1 ... Warm-Up ... 351

    13.2 ... Image Classification ... 354

    13.3 ... Dreamed Images ... 373

    13.4 ... Deployment with Pretrained Networks ... 382

    13.5 ... Summary ... 386

    13.6 ... Further Reading ... 386

    ... Appendices ... 387

    A ... Python in Brief ... 389

    B ... Mathematics in Brief ... 417

    C ... TensorFlow 2 and Keras ... 435

    ... The Authors ... 445

    ... Index ... 447