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Produktbild: Programming Neural Networks with Python

Programming Neural Networks with Python

59,95 €

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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

728 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

728 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