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Produktbild: Developing AI Applications

Developing AI Applications An Introduction

Aus der Reihe Rheinwerk Computing

46,30 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.07.2024

Verlag

Rheinwerk Publishing

Seitenzahl

402

Maße (L/B/H)

24,8/18,2/2,2 cm

Gewicht

738 g

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2601-6

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.07.2024

Verlag

Rheinwerk Publishing

Seitenzahl

402

Maße (L/B/H)

24,8/18,2/2,2 cm

Gewicht

738 g

Auflage

1

Sprache

Englisch

ISBN

978-1-4932-2601-6

Herstelleradresse

Rheinwerk Verlag GmbH
Rheinwerkallee 4
53227 Bonn
DE

Email: service@rheinwerk-verlag.de

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  • Produktbild: Developing AI Applications

  • 1 ... Introduction ... 15






    1.1 ... What Does This Book Offer? ... 15






    1.2 ... What Is Artificial Intelligence? ... 17






    1.3 ... The History of AI: A Brief Overview ... 18






    1.4 ... Development Tools Used in This Book ... 20






    2 ... Installation ... 25






    2.1 ... Anaconda Distribution ... 25






    2.2 ... KNIME ... 30






    3 ... Artificial Neural Networks ... 39






    3.1 ... Classification ... 40






    3.2 ... The Recipe ... 41






    3.3 ... Building ANNs ... 45






    3.4 ... Structure of an Artificial Neuron ... 47






    3.5 ... Feed Forward ... 48






    3.6 ... Back Propagation ... 51






    3.7 ... Updating the Weights ... 53






    3.8 ... ANN for Classification ... 55






    3.9 ... Hyperparameters and Overfitting ... 63






    3.10 ... Dealing with Nonnumerical Data ... 65






    3.11 ... Dealing with Data Gaps ... 67






    3.12 ... Correlation versus Causality ... 69






    3.13 ... Standardization of the Data ... 76






    3.14 ... Regression ... 78






    3.15 ... Deployment ... 81






    3.16 ... Exercises ... 85






    4 ... Decision Trees ... 89






    4.1 ... Simple Decision Trees ... 90






    4.2 ... Boosting ... 100






    4.3 ... XGBoost Regressor ... 109






    4.4 ... Deployment ... 110






    4.5 ... Decision Trees Using Orange ... 111






    4.6 ... Exercises ... 115






    5 ... Convolutional Layers and Images ... 117






    5.1 ... Simple Image Classification ... 118






    5.2 ... Hyperparameter Optimization Using Early Stopping and KerasTuner ... 123






    5.3 ... Convolutional Neural Network ... 128






    5.4 ... Image Classification Using CIFAR-10 ... 134






    5.5 ... Using Pretrained Networks ... 137






    5.6 ... Exercises ... 140






    6 ... Transfer Learning ... 141






    6.1 ... How It Works ... 143






    6.2 ... Exercises ... 150






    7 ... Anomaly Detection ... 151






    7.1 ... Unbalanced Data ... 152






    7.2 ... Resampling ... 156






    7.3 ... Autoencoders ... 158






    7.4 ... Exercises ... 164






    8 ... Text Classification ... 165






    8.1 ... Embedding Layer ... 165






    8.2 ... GlobalAveragePooling1D Layer ... 168






    8.3 ... Text Vectorization ... 170






    8.4 ... Analysis of the Relationships ... 173






    8.5 ... Classifying Large Amounts of Data ... 177






    8.6 ... Exercises ... 180






    9 ... Cluster Analysis ... 181






    9.1 ... Graphical Analysis of the Data ... 182






    9.2 ... The k-Means Clustering Algorithm ... 186






    9.3 ... The Finished Program ... 189






    9.4 ... Exercises ... 192






    10 ... AutoKeras ... 193






    10.1 ... Classification ... 194






    10.2 ... Regression ... 195






    10.3 ... Image Classification ... 196






    10.4 ... Text Classification ... 199






    10.5 ... Exercises ... 202






    11 ... Visual Programming Using KNIME ... 203






    11.1 ... Simple ANNs ... 204






    11.2 ... XGBoost ... 223






    11.3 ... Image Classification Using a Pretrained Model ... 227






    11.4 ... Transfer Learning ... 232






    11.5 ... Autoencoder ... 237






    11.6 ... Text Classification ... 245






    11.7 ... AutoML ... 249






    11.8 ... Cluster Analysis ... 253






    11.9 ... Time Series Analysis ... 257






    11.10 ... Text Generation ... 271






    11.11 ... Further Information on KNIME ... 277






    11.12 ... Exercises ... 278






    12 ... Reinforcement Learning ... 281






    12.1 ... Q-Learning ... 282






    12.2 ... Python Knowledge Required for the Game ... 287






    12.3 ... Trainings ... 292






    12.4 ... Test ... 294






    12.5 ... Outlook ... 295






    12.6 ... Exercises ... 296






    13 ... Genetic Algorithms ... 297






    13.1 ... The Algorithm ... 298






    13.2 ... Example of a Sorted List ... 301






    13.3 ... Example of Equation Systems ... 304






    13.4 ... Real-Life Sample Application ... 306






    13.5 ... Exercises ... 309






    14 ... ChatGPT and GPT-4 ... 311






    14.1 ... Prompt Engineering ... 313






    14.2 ... The ChatGPT Programming Interface ... 328






    14.3 ... Exercise 1: Math Support ... 344






    15 ... DALL-E and Successor Models ... 345






    15.1 ... DALL-E 2 ... 345






    15.2 ... DALL-E 3 ... 350






    15.3 ... Programming Interface ... 352






    15.4 ... Exercise 1: DALL-E API with Moderation ... 357






    16 ... Outlook ... 359






    ... Appendices ... 361






    A ... Exercise Solutions ... 363






    A.1 ... Chapter 3 ... 363






    A.2 ... Chapter 4 ... 368






    A.3 ... Chapter 6 ... 371



    A.4 ... Chapter 7 ... 373



    A.5 ... Chapter 8 ... 376



    A.6 ... Chapter 9 ... 379



    A.7 ... Chapter 10 ... 381



    A.8 ... Chapter 11 ... 384



    A.9 ... Chapter 12 ... 389



    A.10 ... Chapter 13 ... 390



    A.11 ... Chapter 14 ... 392



    A.12 ... Chapter 15 ... 393



    B ... References ... 395






    C ... The Author ... 397






    ... Index ... 399